Means and Methods for Diagnosing Heart Failure in a Subject

ABSTRACT

The present invention relates to the field of diagnostic methods. Specifically, the present invention contemplates a method for diagnosing heart failure in a subject, a method for identifying whether a subject is in need for a therapy of heart failure or a method for determining whether a heart failure therapy is successful. The invention also relates to tools for carrying out the aforementioned methods, such as diagnostic devices.

The present invention relates to the field of diagnostic methods.Specifically, the present invention contemplates a method for diagnosingheart failure in a subject, a method for identifying whether a subjectis in need for a therapy of heart failure or a method for determiningwhether a heart failure therapy is successful. The invention alsorelates to tools for carrying out the aforementioned methods, such asdiagnostic devices.

Heart failure is a severe problem in modern medicine. The impairedfunction of the heart can give rise to life-threatening conditions andresults in discomfort for the patients suffering from heart failure.Heart failure can affect the right or the left heart, respectively, andcan vary in strength. A classification system was originally developedby the New York Heart association (NYHA). According to theclassification system, the mild cases of heart failure are categorizedas class I cases. These patients only show symptoms under extremeexercise. The intermediate cases show more pronounced symptoms alreadyunder less exercise (classes II and III) while class IV, shows alreadysymptoms at rest (New York Heart Association. Diseases of the heart andblood vessels. Nomenclature and criteria for diagnosis, 6^(th) ed.Boston: Little, Brown and co, 1964; 114).

The prevalence of heart failure steadily increases in the population ofthe western developed countries over the last years. One reason for saidincrease can be seen in an increased average life expectation due tomodern medicine. The mortality rate caused by heart failure, however,could be further reduced by improved diagnostic and therapeuticapproaches. The so-called “Framingham” study reported a reduction of the5 year mortality from 70% to 59% in men and from 57% to 45% in womenwhen comparing a time window of 1950 to 1969 with 1990 to 1999. The“Mayo” study shows a reduction from 65% to 50% for men for a time windowof 1996 to 2000 compared to 1979 to 1984 and from 51% to 46% for women.Notwithstanding this reduction of the mortality rate, the overallmortality due to heart failure is still a major burden to societies.One-year mortality for NYHA class II to III patients under ACE inhibitortherapy is still between 9-12% (SOLVED) and for NYHA class IV withoutACE inhibitor therapy 52% (Consensus).

Diagnostic techniques such as echocardiography are dependent on theexperience of the individual investigator and, thus, not alwaysreliable. Moreover, these techniques sometimes fail to diagnose theearly onset of heart failure. Biochemical assays which are based oncardiac hormones such as Brain natriuretic peptides (BNP) are alsoinfluenced by other diseases and disorders such as renal insufficiencyor depend on the overall physical condition of the patient.Nevertheless, Brain natriuretic peptides are the current gold standardfor biochemically assessing heart failure. According to a recent studycomparing BNP and N-terminal pro-BNP (NT-proBNP) in the diagnosis ofheart failure, BNP is a better indicator for heart failure and leftventricular systolic dysfunction than NT-proBNP. In groups ofsymptomatic patients, a diagnostic odds ratio of 27 for BNP compareswith a sensitivity of 85% and specificity of 84% in detecting heartfailure (Ewald 2008, Intern Med J 38 (2):101-13.).

However, it is a goal of modern medicine to reliably identify and treatpatients with heart failure and, in particular, to identify them at theearly onset of heart failure, i.e. at the early NYHA stages I to III andin particular at NYHA stage I. Accordingly, means and methods forreliably diagnosing heart failure are highly desired but not yetavailable.

Therefore, the present invention pertains to a method for diagnosingheart failure in a subject comprising the steps of:

-   -   a) determining in a sample of a subject suspected to suffer from        heart failure the amount of at least one biomarker selected from        the biomarkers listed in Table 1a to c, 3, 4a to c or 6;    -   b) comparing the amount of the said at least one biomarker to a        reference, whereby heart failure is to be diagnosed.

The method as referred to in accordance with the present inventionincludes a method which essentially consists of the aforementioned stepsor a method which includes further steps. However, it is to beunderstood that the method, in a preferred embodiment, is a methodcarried out ex vivo, i.e. not practised on the human or animal body. Themethod, preferably, can be assisted by automation.

The term “diagnosing” as used herein refers to assessing whether asubject suffers from the heart failure, or not. As will be understood bythose skilled in the art, such an assessment, although preferred to be,may usually not be correct for 100% of the investigated subjects. Theterm, however, requires that a statistically significant portion ofsubjects can be correctly assessed and, thus, diagnosed. Whether aportion is statistically significant can be determined without furtherado by the person skilled in the art using various well known statisticevaluation tools, e.g., determination of confidence intervals, p-valuedetermination, Student's t-test, Mann-Whitney test, etc. Details arefound in Dowdy and Wearden, Statistics for Research, John Wiley & Sons,New York 1983. Preferred confidence intervals are at least 50%, at least60%, at least 70%, at least 80%, at least 90% or at least 95%. Thep-values are, preferably, 0.2, 0.1, or 0.05.

The term includes individual diagnosis of heart failure or its symptomsas well as continuous monitoring of a patient. Monitoring, i.e.diagnosing the presence or absence of heart failure or the symptomsaccompanying it at various time points, includes monitoring of patientsknown to suffer from heart failure as well as monitoring of subjectsknown to be at risk of developing heart failure. Furthermore, monitoringcan also be used to determine whether a patient is treated successfullyor whether at least symptoms of heart failure can be ameliorated overtime by a certain therapy. Moreover, the term also includes classifyinga subject according to the New York Heart Association (NYHA) classes forheart failure. According to this classification, heart failure can besubdivided into four classes. Subjects exhibiting class I show nolimitation in activities except under strong physical exercise. Subjectsexhibiting class II show slight, mild limitation of activity, whilecomfortable at rest or under mild exertion. Subjects exhibiting classIII show marked limitation of any activity, while comfortable only atrest. Subjects exhibiting class IV show discomfort and symptoms even atrest. Preferably, heart failure to be determined in accordance with thepresent invention is mild heart failure, i.e. heart failure according toNYHA class I, or intermediated heart failure, i.e. heart failureaccording to NYHA class II and/or III. Preferably, said heart failure isheart failure according to NYHA class I and the said at least onebiomarker is selected from Table 4a to c or 6. Also preferably, saidheart failure is heart failure according to NYHA class II or III and theat least one biomarker is selected from Table 1a to c or 3.

Another staging system is provided by the American Heart Association.Four stages of heart failure are subdivided: Stage A: Patients at highrisk for developing HF in the future but no functional or structuralheart disorder. Stage B: a structural heart disorder but no symptoms atany stage. Stage C: previous or current symptoms of heart failure in thecontext of an underlying structural heart problem, but managed withmedical treatment. Stage D: advanced disease requiring hospital-basedsupport, a heart transplant or palliative care. It will be understoodthat the method of the present invention can also be used for stagingheart failure according to this system, preferably, the identifiedbiomarkers shall allow to diagnose heart failure according to stages Ato C and to discriminate between the mild stage A (Table 4a to c or 6)and the more severe stages B and C (Table 1a to c or 3).

The term “heart failure” as used herein relates to an impaired functionof the heart. The said impairment can be a systolic dysfunctionresulting in a significantly reduced ejection fraction of blood from theheart and, thus, a reduced blood flow. Specifically, systolic heartfailure is characterized by a significantly reduced left ventricularejection fraction (LEVF), preferably, an ejection fraction of less than55%. Alternatively, the impairment can be a diastolic dysfunction, i.e.a failure of the ventricle to properly relax. The latter is usuallyaccompanied by a stiffer ventricular wall. The diastolic dysfunctioncauses inadequate filling of the ventricle and, therefore, results inconsequences for the blood flow, in general. Thus, diastolic dysfunctionalso results in elevated end-diastolic pressures, and the end result iscomparable to the case of systolic dysfunction (pulmonary edema in leftheart failure, peripheral edema in right heart failure.) Heart failuremay, thus, affect the right heart (pulmonary circulation), the leftheart (body circulation) or both. Techniques for measuring an impairedheart function and, thus, heart failure, are well known in the art andinclude echocardiography, electrophysiology, angiography, and thedetermination of peptide biomarkers, such as the Brain NatriureticPeptide (BNP) or the N-terminal fragment of its propeptide, in theblood. It will be understood that the impaired function of the heart canoccur permanently or only under certain stress or exercise conditions.Dependent on the strength of the symptoms, heart failure can beclassified as set forth elsewhere herein. Typical symptoms of heartfailure include dyspnea, chest pain, dizziness, confusion, pulmonaryand/or peripheral edema. It will be understood that the occurrence ofthe symptoms as well as their severity may depended on the severity ofheart failure and the characteristics and causes of the heart failure,systolic or diastolic or restrictive i.e. right or left heart locatedheart failure. Further symptoms of heart failure are well known in theart and are described in the standard text books of medicine, such asStedman or Brunnwald.

Preferably, heart failure as used herein relates to congestive heartfailure and, more preferably, the subject exhibiting said heart failuresuffers from a dilatative cardiomyopathy. Also more preferably, thesubject in accordance with the present invention suffers from ischemiccardiomyopathy or hypertrophic cardiomyopathy. In another embodiment thesubject in accordance with the present invention suffers from dilatativecardiomyopathy and ischemic cardiomyopathy or from dilatativecardiomyopathy and hypertrophic cardiomyopathy. However, heart failureas referred to in accordance with the present invention also includesischemic heart failure, myocardial hypertrophy, valvular heart disease,restrictive cardiomyopathy, constrictive pericardial disorders, andhypertensive disease.

The term “biomarker” as used herein refers to a molecular species whichserves as an indicator for a disease or effect as referred to in thisspecification. Said molecular species can be a metabolite itself whichis found in a sample of a subject. Moreover, the biomarker may also be amolecular species which is derived from said metabolite. In such a case,the actual metabolite will be chemically modified in the sample orduring the determination process and, as a result of said modification,a chemically different molecular species, i.e. the analyte, will be thedetermined molecular species. It is to be understood that in such acase, the analyte represents the actual metabolite and has the samepotential as an indicator for the respective medical condition.

In the method according to the present invention, at least onemetabolite of the aforementioned group of biomarkers, i.e. thebiomarkers as shown in Tables 1a to c, 3, 4a to c and/or 6, is to bedetermined. However, more preferably, a group of biomarkers will bedetermined in order to strengthen specificity and/or sensitivity of theassessment. Such a group, preferably, comprises at least 2, at least 3,at least 4, at least 5, at least 10 or up to all of the said biomarkersshown in the Tables. In addition to the specific biomarkers recited inthe specification, other biomarkers may be, preferably, determined aswell in the methods of the present invention.

A metabolite as used herein refers to at least one molecule of aspecific metabolite up to a plurality of molecules of the said specificmetabolite. It is to be understood further that a group of metabolitesmeans a plurality of chemically different molecules wherein for eachmetabolite at least one molecule up to a plurality of molecules may bepresent. A metabolite in accordance with the present inventionencompasses all classes of organic or inorganic chemical compoundsincluding those being comprised by biological material such asorganisms. Preferably, the metabolite in accordance with the presentinvention is a small molecule compound. More preferably, in case aplurality of metabolites is envisaged, said plurality of metabolitesrepresenting a metabolome, i.e. the collection of metabolites beingcomprised by an organism, an organ, a tissue, a body fluid or a cell ata specific time and under specific conditions.

The metabolites are small molecule compounds, such as substrates forenzymes of metabolic pathways, intermediates of such pathways or theproducts obtained by a metabolic pathway. Metabolic pathways are wellknown in the art and may vary between species. Preferably, said pathwaysinclude at least citric acid cycle, respiratory chain, glycolysis,gluconeogenesis, hexose monophosphate pathway, oxidative pentosephosphate pathway, production and β-oxidation of fatty acids, ureacycle, amino acid biosynthesis pathways, protein degradation pathwayssuch as proteasomal degradation, amino acid degrading pathways,biosynthesis or degradation of: lipids, polyketides (including e.g.flavonoids and isoflavonoids), isoprenoids (including eg. terpenes,sterols, steroids, carotenoids, xanthophylls), carbohydrates,phenylpropanoids and derivatives, alcaloids, benzenoids, indoles,indole-sulfur compounds, porphyrines, anthocyans, hormones, vitamins,cofactors such as prosthetic groups or electron carriers, lignin,glucosinolates, purines, pyrimidines, nucleosides, nucleotides andrelated molecules such as tRNAs, microRNAs (miRNA) or mRNAs.Accordingly, small molecule compound metabolites are preferably composedof the following classes of compounds: alcohols, alkanes, alkenes,alkines, aromatic compounds, ketones, aldehydes, carboxylic acids,esters, amines, imines, amides, cyanides, amino acids, peptides, thiols,thioesters, phosphate esters, sulfate esters, thioethers, sulfoxides,ethers, or combinations or derivatives of the aforementioned compounds.The small molecules among the metabolites may be primary metaboliteswhich are required for normal cellular function, organ function oranimal growth, development or health. Moreover, small moleculemetabolites further comprise secondary metabolites having essentialecological function, e.g. metabolites which allow an organism to adaptto its environment. Furthermore, metabolites are not limited to saidprimary and secondary metabolites and further encompass artificial smallmolecule compounds. Said artificial small molecule compounds are derivedfrom exogenously provided small molecules which are administered ortaken up by an organism but are not primary or secondary metabolites asdefined above. For instance, artificial small molecule compounds may bemetabolic products obtained from drugs by metabolic pathways of theanimal. Moreover, metabolites further include peptides, oligopeptides,polypeptides, oligonucleotides and polynucleotides, such as RNA or DNA.More preferably, a metabolite has a molecular weight of 50 Da (Dalton)to 30,000 Da, most preferably less than 30,000 Da, less than 20,000 Da,less than 15,000 Da, less than 10,000 Da, less than 8,000 Da, less than7,000 Da, less than 6,000 Da, less than 5,000 Da, less than 4,000 Da,less than 3,000 Da, less than 2,000 Da, less than 1,000 Da, less than500 Da, less than 300 Da, less than 200 Da, less than 100 Da.Preferably, a metabolite has, however, a molecular weight of at least 50Da. Most preferably, a metabolite in accordance with the presentinvention has a molecular weight of 50 Da up to 1,500 Da.

The term “sample” as used herein refers to samples from body fluids,preferably, blood, plasma, serum, saliva or urine, or samples derived,e.g., by biopsy, from cells, tissues or organs, in particular from theheart. More preferably, the sample is a blood, plasma or serum sample,most preferably, a plasma sample. Biological samples can be derived froma subject as specified elsewhere herein. Techniques for obtaining theaforementioned different types of biological samples are well known inthe art. For example, blood samples may be obtained by blood takingwhile tissue or organ samples are to be obtained, e.g., by biopsy.

The aforementioned samples are, preferably, pre-treated before they areused for the method of the present invention. As described in moredetail below, said pre-treatment may include treatments required torelease or separate the compounds or to remove excessive material orwaste. Suitable techniques comprise centrifugation, extraction,fractioning, ultrafiltration, protein precipitation followed byfiltration and purification and/or enrichment of compounds. Moreover,other pre-treatments are carried out in order to provide the compoundsin a form or concentration suitable for compound analysis. For example,if gas-chromatography coupled mass spectrometry is used in the method ofthe present invention, it will be required to derivatize the compoundsprior to the said gas chromatography. Suitable and necessarypre-treatments depend on the means used for carrying out the method ofthe invention and are well known to the person skilled in the art.Pre-treated samples as described before are also comprised by the term“sample” as used in accordance with the present invention.

The term “subject” as used herein relates to animals and, preferably, tomammals. More preferably, the subject is a primate and, most preferably,a human. The subject, preferably, is suspected to suffer from heartfailure, i.e. it may already show some or all of the symptoms associatedwith the disease. More preferably, it exhibits symptoms according to anyone of NYHA classes I to III. Moreover, the subject shall alsopreferably exhibit congestive systolic heart failure due to contractiledysfunction such as dilated cardiomyopathy. Preferably, the subject,however, is besides the aforementioned diseases and disorders apparentlyhealthy. In particular, it shall, preferably, not exhibit symptomsaccording to NYHA class IV patients or suffer from stroke, myocardialinfarction within the last 4 month before the sample has been taken orfrom acute or chronic inflammatory diseases and malignant tumors.Furthermore, the subject is preferably in stable medications within thelast 4 weeks before the sample was taken.

The term “determining the amount” as used herein refers to determiningat least one characteristic feature of a biomarker to be determined bythe method of the present invention in the sample. Characteristicfeatures in accordance with the present invention are features whichcharacterize the physical and/or chemical properties includingbiochemical properties of a biomarker. Such properties include, e.g.,molecular weight, viscosity, density, electrical charge, spin, opticalactivity, colour, fluorescence, chemoluminescence, elementarycomposition, chemical structure, capability to react with othercompounds, capability to elicit a response in a biological read outsystem (e.g., induction of a reporter gene) and the like. Values forsaid properties may serve as characteristic features and can bedetermined by techniques well known in the art. Moreover, thecharacteristic feature may be any feature which is derived from thevalues of the physical and/or chemical properties of a biomarker bystandard operations, e.g., mathematical calculations such asmultiplication, division or logarithmic calculus. Most preferably, theat least one characteristic feature allows the determination and/orchemical identification of the said at least one biomarker and itsamount. Accordingly, the characteristic value, preferably, alsocomprises information relating to the abundance of the biomarker fromwhich the characteristic value is derived. For example, a characteristicvalue of a biomarker may be a peak in a mass spectrum. Such a peakcontains characteristic information of the biomarker, i.e. the m/zinformation, as well as an intensity value being related to theabundance of the said biomarker (i.e. its amount) in the sample.

As discussed before, each biomarker comprised by a sample may be,preferably, determined in accordance with the present inventionquantitatively or semi-quantitatively. For quantitative determination,either the absolute or precise amount of the biomarker will bedetermined or the relative amount of the biomarker will be determinedbased on the value determined for the characteristic feature(s) referredto herein above. The relative amount may be determined in a case werethe precise amount of a biomarker can or shall not be determined. Insaid case, it can be determined whether the amount in which thebiomarker is present is enlarged or diminished with respect to a secondsample comprising said biomarker in a second amount. In a preferredembodiment said second sample comprising said biomarker shall be acalculated reference as specified elsewhere herein. Quantitativelyanalysing a biomarker, thus, also includes what is sometimes referred toas semi-quantitative analysis of a biomarker.

Moreover, determining as used in the method of the present invention,preferably, includes using a compound separation step prior to theanalysis step referred to before. Preferably, said compound separationstep yields a time resolved separation of the metabolites comprised bythe sample. Suitable techniques for separation to be used preferably inaccordance with the present invention, therefore, include allchromatographic separation techniques such as liquid chromatography(LC), high performance liquid chromatography (HPLC), gas chromatography(GC), thin layer chromatography, size exclusion or affinitychromatography. These techniques are well known in the art and can beapplied by the person skilled in the art without further ado. Mostpreferably, LC and/or GC are chromatographic techniques to be envisagedby the method of the present invention. Suitable devices for suchdetermination of biomarkers are well known in the art. Preferably, massspectrometry is used in particular gas chromatography mass spectrometry(GC-MS), liquid chromatography mass spectrometry (LC-MS), directinfusion mass spectrometry or Fourier transform ion-cyclotrone-resonancemass spectrometry (FT-ICR-MS), capillary electrophoresis massspectrometry (CE-MS), high-performance liquid chromatography coupledmass spectrometry (HPLC-MS), quadrupole mass spectrometry, anysequentially coupled mass spectrometry, such as MS-MS or MS-MS-MS,inductively coupled plasma mass spectrometry (ICP-MS), pyrolysis massspectrometry (Py-MS), ion mobility mass spectrometry or time of flightmass spectrometry (TOF). Most preferably, LC-MS and/or GC-MS are used asdescribed in detail below. Said techniques are disclosed in, e.g.,Nissen 1995, Journal of Chromatography A, 703: 37-57, U.S. Pat. No.4,540,884 or U.S. Pat. No. 5,397,894, the disclosure content of which ishereby incorporated by reference. As an alternative or in addition tomass spectrometry techniques, the following techniques may be used forcompound determination: nuclear magnetic resonance (NMR), magneticresonance imaging (MRI), Fourier transform infrared analysis (FT-IR),ultraviolet (UV) spectroscopy, refraction index (RI), fluorescentdetection, radiochemical detection, electrochemical detection, lightscattering (LS), dispersive Raman spectroscopy or flame ionisationdetection (FID). These techniques are well known to the person skilledin the art and can be applied without further ado. The method of thepresent invention shall be, preferably, assisted by automation. Forexample, sample processing or pre-treatment can be automated byrobotics. Data processing and comparison is, preferably, assisted bysuitable computer programs and databases. Automation as described hereinbefore allows using the method of the present invention inhigh-throughput approaches.

Moreover, the at least one biomarker can also be determined by aspecific chemical or biological assay. Said assay shall comprise meanswhich allow to specifically detect the at least one biomarker in thesample. Preferably, said means are capable of specifically recognizingthe chemical structure of the biomarker or are capable of specificallyidentifying the biomarker based on its capability to react with othercompounds or its capability to elicit a response in a biological readout system (e.g., induction of a reporter gene). Means which are capableof specifically recognizing the chemical structure of a biomarker are,preferably, antibodies or other proteins which specifically interactwith chemical structures, such as receptors or enzymes. Specificantibodies, for instance, may be obtained using the biomarker as antigenby methods well known in the art. Antibodies as referred to hereininclude both polyclonal and monoclonal antibodies, as well as fragmentsthereof, such as Fv, Fab and F(ab)₂ fragments that are capable ofbinding the antigen or hapten. The present invention also includeshumanized hybrid antibodies wherein amino acid sequences of a non-humandonor antibody exhibiting a desired antigen-specificity are combinedwith sequences of a human acceptor antibody. Moreover, encompassed aresingle chain antibodies. The donor sequences will usually include atleast the antigen-binding amino acid residues of the donor but maycomprise other structurally and/or functionally relevant amino acidresidues of the donor antibody as well. Such hybrids can be prepared byseveral methods well known in the art. Suitable proteins which arecapable of specifically recognizing the biomarker are, preferably,enzymes which are involved in the metabolic conversion of the saidbiomarker. Said enzymes may either use the biomarker as a substrate ormay convert a substrate into the biomarker. Moreover, said antibodiesmay be used as a basis to generate oligopeptides which specificallyrecognize the biomarker. These oligopeptides shall, for example,comprise the enzyme's binding domains or pockets for the said biomarker.Suitable antibody and/or enzyme based assays may be RIA(radioimmunoassay), ELISA (enzyme-linked immunosorbent assay), sandwichenzyme immune tests, electro-chemiluminescence sandwich immunoassays(ECLIA), dissociation-enhanced lanthanide fluoro immuno assay (DELFIA)or solid phase immune tests. Moreover, the biomarker may also bedetermined based on its capability to react with other compounds, i.e.by a specific chemical reaction. Further, the biomarker may bedetermined in a sample due to its capability to elicit a response in abiological read out system. The biological response shall be detected asread out indicating the presence and/or the amount of the biomarkercomprised by the sample. The biological response may be, e.g., theinduction of gene expression or a phenotypic response of a cell or anorganism. In a preferred embodiment the determination of the least onebiomarker is a quantitative process, e.g., allowing also thedetermination of the amount of the at least one biomarker in the sample.

As described above, said determining of the at least one biomarker can,preferably, comprise mass spectrometry (MS). Mass spectrometry as usedherein encompasses all techniques which allow for the determination ofthe molecular weight (i.e. the mass) or a mass variable corresponding toa compound, i.e. a biomarker, to be determined in accordance with thepresent invention. Preferably, mass spectrometry as used herein relatesto GC-MS, LC-MS, direct infusion mass spectrometry, FT-ICR-MS, CE-MS,HPLC-MS, quadrupole mass spectrometry, any sequentially coupled massspectrometry such as MS-MS or MS-MS-MS, ICP-MS, Py-MS, TOF or anycombined approaches using the aforementioned techniques. How to applythese techniques is well known to the person skilled in the art.Moreover, suitable devices are commercially available. More preferably,mass spectrometry as used herein relates to LC-MS and/or GC-MS, i.e. tomass spectrometry being operatively linked to a prior chromatographicseparation step. More preferably, mass spectrometry as used hereinencompasses quadrupole MS. Most preferably, said quadrupole MS iscarried out as follows: a) selection of a mass/charge quotient (m/z) ofan ion created by ionisation in a first analytical quadrupole of themass spectrometer, b) fragmentation of the ion selected in step a) byapplying an acceleration voltage in an additional subsequent quadrupolewhich is filled with a collision gas and acts as a collision chamber, c)selection of a mass/charge quotient of an ion created by thefragmentation process in step b) in an additional subsequent quadrupole,whereby steps a) to c) of the method are carried out at least once andanalysis of the mass/charge quotient of all the ions present in themixture of substances as a result of the ionisation process, whereby thequadrupole is filled with collision gas but no acceleration voltage isapplied during the analysis. Details on said most preferred massspectrometry to be used in accordance with the present invention can befound in WO 03/073464.

More preferably, said mass spectrometry is liquid chromatography (LC) MSand/or gas chromatography (GC) MS. Liquid chromatography as used hereinrefers to all techniques which allow for separation of compounds (i.e.metabolites) in liquid or supercritical phase. Liquid chromatography ischaracterized in that compounds in a mobile phase are passed through thestationary phase. When compounds pass through the stationary phase atdifferent rates they become separated in time since each individualcompound has its specific retention time (i.e. the time which isrequired by the compound to pass through the system). Liquidchromatography as used herein also includes HPLC. Devices for liquidchromatography are commercially available, e.g. from AgilentTechnologies, USA. Gas chromatography as applied in accordance with thepresent invention, in principle, operates comparable to liquidchromatography. However, rather than having the compounds (i.e.metabolites) in a liquid mobile phase which is passed through thestationary phase, the compounds will be present in a gaseous volume. Thecompounds pass the column which may contain solid support materials asstationary phase or the walls of which may serve as or are coated withthe stationary phase. Again, each compound has a specific time which isrequired for passing through the column. Moreover, in the case of gaschromatography it is preferably envisaged that the compounds arederivatised prior to gas chromatography. Suitable techniques forderivatisation are well known in the art. Preferably, derivatisation inaccordance with the present invention relates to methoxymation andtrimethylsilylation of, preferably, polar compounds andtransmethylation, methoxymation and trimethylsilylation of, preferably,non-polar (i.e. lipophilic) compounds,

The term “reference” refers to values of characteristic features of eachof the biomarker which can be correlated to a medical condition, i.e.the presence or absence of the disease, diseases status or an effectreferred to herein. Preferably, a reference is a threshold value (e.g.,an amount or ratio of amounts) for a biomarker whereby values found in asample to be investigated which are higher than or essentially identicalto the threshold are indicative for the presence of a medical conditionwhile those being lower are indicative for the absence of the medicalcondition. It will be understood that also preferably, a reference maybe a threshold value for a biomarker whereby values found in a sample tobe investigated which are lower or identical than the threshold areindicative for the presence of a medical condition while those beinghigher are indicative for the absence of the medical condition.

In accordance with the aforementioned method of the present invention, areference is, preferably, a reference obtained from a sample from asubject or group of subjects known to suffer from heart failure. In sucha case, a value for the at least one biomarker found in the test samplebeing essentially identical is indicative for the presence of thedisease. Moreover, the reference, also preferably, could be from asubject or group of subjects known not to suffer from heart failure,preferably, an apparently healthy subject. In such a case, a value forthe at least one biomarker found in the test sample being altered withrespect to the reference is indicative for the presence of the disease.The same applies mutatis mutandis for a calculated reference, mostpreferably the average or median, for the relative or absolute value ofthe at least one biomarker of a population of individuals comprising thesubject to be investigated. The absolute or relative values of the atleast one biomarker of said individuals of the population can bedetermined as specified elsewhere herein. How to calculate a suitablereference value, preferably, the average or median, is well known in theart. The population of subjects referred to before shall comprise aplurality of subjects, preferably, at least 5, 10, 50, 100, 1,000 or10,000 subjects. It is to be understood that the subject to be diagnosedby the method of the present invention and the subjects of the saidplurality of subjects are of the same species.

The value for the at least one biomarker of the test sample and thereference values are essentially identical, if the values for thecharacteristic features and, in the case of quantitative determination,the intensity values are essentially identical. Essentially identicalmeans that the difference between two values is, preferably, notsignificant and shall be characterized in that the values for theintensity are within at least the interval between 1st and 99^(th)percentile, 5^(th) and 95^(th) percentile, 10^(th) and 90^(th)percentile, 20^(th) and 80^(th) percentile, 30^(th) and 70^(th)percentile, 40^(th) and 60^(th) percentile of the reference value,preferably, the 50^(th), 60^(th), 70^(th), 80^(th), 90^(th) or 95^(th)percentile of the reference value. Statistical test for determiningwhether two amounts are essentially identical are well known in the artand are also described elsewhere herein.

An observed difference for two values, on the other hand, shall bestatistically significant. A difference in the relative or absolutevalue is, preferably, significant outside of the interval between45^(th) and 55^(th) percentile, 40^(th) and 60^(th) percentile, 30^(th)and 70^(th) percentile, 20^(th) and 80^(th) percentile, 10^(th) and90^(th) percentile, 5^(th) and 95^(th) percentile, 1st and 99^(th)percentile of the reference value. Preferred changes and ratios of themedians are described in the accompanying Tables as well as in theExamples.

Preferably, the reference, i.e. values for at least one characteristicfeature of the at least one biomarker or ratios thereof, will be storedin a suitable data storage medium such as a database and are, thus, alsoavailable for future assessments.

The term “comparing” refers to determining whether the determined valueof a biomarker is essentially identical to a reference or differstherefrom. Preferably, a value for a biomarker is deemed to differ froma reference if the observed difference is statistically significantwhich can be determined by statistical techniques referred to elsewherein this description. If the difference is not statistically significant,the biomarker value and the reference are essentially identical. Basedon the comparison referred to above, a subject can be assessed to sufferfrom the disease, or not.

For the specific biomarkers referred to in this specification, preferredvalues for the changes in the relative amounts or ratios (i.e. thechanges expressed as the ratios of the medians) or the kind ofregulation (i.e. “up”- or “down”-regulation resulting in a higher orlower relative and/or absolute amount or ratio) are indicated in theTables and in the Examples below. The median of ratios indicates thedegree of increase or decrease, e.g., a value of 2 means that the amountis twice the amount of the biomarker compared to the reference.Moreover, it is apparent whether there is an “up-regulation” or a“down-regulation”. In the case of an “up-regulation” the ratio of medianshall exceed 1.0 while it will be below 1.0 in case of a“down”-regulation. Accordingly, the direction of regulation can bederived from the Tables as well.

The comparison is, preferably, assisted by automation. For example, asuitable computer program comprising algorithms for the comparison oftwo different data sets (e.g., data sets comprising the values of thecharacteristic feature(s)) may be used. Such computer programs andalgorithms are well known in the art. Notwithstanding the above, acomparison can also be carried out manually.

Advantageously, it has been found in the study underlying the presentinvention that the amounts of the specific biomarkers referred to aboveare indicators for heart failure. Accordingly, the at least onebiomarker as specified above in a sample can, in principle, be used forassessing whether a subject suffers from heart failure. This isparticularly helpful for an efficient diagnosis of the disease as wellas for improving of the pre-clinical and clinical management of heartfailure as well as an efficient monitoring of patients. Moreover, thefindings underlying the present invention will also facilitate thedevelopment of efficient drug-based therapies or other interventionsincluding nutritional diets against heart failure as set forth in detailbelow.

In a preferred embodiment of the method of the present invention, saidsample of the subject has been obtained at rest and said at least onebiomarker is selected from Table 1a to c or 4a to c.

In a further preferred embodiment of the method of the presentinvention, said sample of the subject has been obtained under exerciseand said at least one biomarker is selected from Table 3 or 6.

The term “exercise” as used herein refers to applying load of work tothe subject. Preferably, the said work load is a permanent work load.Applying such a permanent work load can be achieved by spiroergometry asdescribed in the accompanying Examples below, in detail. Preferably, theload of work applied to the subject is constantly increased. A preferredway for exercise is described in the accompanying Examples, below. Thesample of the subject to be investigated by the method of the presentinvention is, preferably, obtained at the peak of exercise (see alsoExamples, below). In another preferred embodiment of the method of thepresent invention, said heart failure is dilated cardiomyopathy and theat least one biomarker is selected from Table 1a or 4a.

In yet another preferred embodiment of the method of the presentinvention, said heart failure is ischemic cardiomyopathy and/ordilatative cardiomyopathy and the at least one biomarker is selectedfrom Table 1b or 4b.

In another preferred embodiment of the method of the present invention,said heart failure is hypertrophic cardiomyopathy and/or dilatativecardiomyopathy and the at least one biomarker is selected from Table 1cor 4c.

The definitions and explanations of the terms made above apply mutatismutandis for the following embodiments of the present invention exceptspecified otherwise herein below.

The present invention also contemplates a method for diagnosing heartfailure in a subject comprising the steps of:

-   -   a) determining in a first and a second sample of a subject        suspected to suffer from heart failure the amount of at least        one biomarker selected from the biomarkers listed in Table 2 or        5, wherein said first sample has been obtained at rest and said        second sample has been obtained under exercise;    -   b) calculating a ration of the amount of the at least one        biomarker determined in the first and the second sample; and    -   c) comparing the calculated ratio to a reference, whereby heart        failure is to be diagnosed.

Te term “calculating” as used herein refers to calculating the ration ofthe amount of the at least one biomarker determined in the second sample(t1) and the amount of the at least one biomarker determined in thefirst sample (t0), i.e. t1/t0. It will be understood that the termcalculating also encompasses other mathematical operations which resultin a parameter which is correlated to the said ratio.

It will be understood that a reference in case of the aforementionedmethod of the present invention shall be a reference ratio, i.e.preferably a ratio (t1/t0) derived from either a subject or group ofsubjects known to suffer not from heart failure or a subject or group ofsubjects known to suffer from heart failure. An essential difference inthe ratio in the first case indicates the presence heart failure whilean essential difference in the second case, preferably, indicates theabsence of heart failure. Likewise, essentially identical ratios in thefirst case (i.e. the comparison to a reference ratio from a healthysubject) indicates the absence of heart failure while in the secondcase, an essentially identical ratio indicates the presence of heartfailure. In addition, the further explanations and definitions made forreferences above, apply accordingly.

In a preferred embodiment of the aforementioned method of the presentinvention, said heart failure is mild to moderate heart failureaccording to NYHA class I and the said at least one biomarker isselected from Table 5.

The present invention also relates to a method for identifying whether asubject is in need for a therapy of heart failure or a change of therapycomprising the steps of the methods of the present invention and thefurther step of identifying a subject in need if heart failure isdiagnosed.

The phrase “in need for a therapy of heart failure” as used herein meansthat the disease in the subject is in a status where therapeuticintervention is necessary or beneficial in order to ameliorate or treatheart failure or the symptoms associated therewith. Accordingly, thefindings of the studies underlying the present invention do not onlyallow diagnosing heart failure in a subject but also allow foridentifying subjects which should be treated by an heart failure therapyor whose heart failure therapy needs adjustment. Once the subject hasbeen identified, the method may further include a step of makingrecommendations for a therapy of heart failure.

A therapy of heart failure as used in accordance with the presentinvention, preferably, relates to a therapy which comprises or consistsof the administration of at least one drug selected from the groupconsisting of: ACE Inhibitors (ACEI), Beta Blockers, AT1-Inhibitors,Aldosteron Antagonists, Renin Antagonists, Diuretics, Ca-Sensitizer,Digitalis Glykosides, polypeptides of the protein S100 family (asdisclosed by DE000003922873A1, DE000019815128A1 or DE000019915485A1hereby incorporated by reference), natriuretic peptides such as BNP(Nesiritide (human recombinant Brain Natriuretic Peptide-BNP)) or ANP.

The present invention further relates to a method for determiningwhether a therapy against heart failure is successful in a subjectcomprising the steps of the methods of the present invention and thefurther step of determining whether a therapy is successful if no heartfailure is diagnosed.

It is to be understood that a heart failure therapy will be successfulif heart failure or at least some symptoms thereof can be treated orameliorated compared to an untreated subject. Moreover, a therapy isalso successful as meant herein if the disease progression can beprevented or at least slowed down compared to an untreated subject.

The aforementioned methods for the determination of the at least onebiomarker can be implemented into a device. A device as used hereinshall comprise at least the aforementioned means. Moreover, the device,preferably, further comprises means for comparison and evaluation of thedetected characteristic feature(s) of the at least one biomarker and,also preferably, the determined signal intensity. The means of thedevice are, preferably, operatively linked to each other. How to linkthe means in an operating manner will depend on the type of meansincluded into the device. For example, where means for automaticallyqualitatively or quantitatively determining the biomarker are applied,the data obtained by said automatically operating means can be processedby, e.g., a computer program in order to facilitate the assessment.Preferably, the means are comprised by a single device in such a case.Said device may accordingly include an analyzing unit for the biomarkerand a computer unit for processing the resulting data for theassessment. Preferred devices are those which can be applied without theparticular knowledge of a specialized clinician, e.g., electronicdevices which merely require loading with a sample.

Alternatively, the methods for the determination of the at least onebiomarker can be implemented into a system comprising several deviceswhich are, preferably, operatively linked to each other. Specifically,the means must be linked in a manner as to allow carrying out the methodof the present invention as described in detail above. Therefore,operatively linked, as used herein, preferably, means functionallylinked. Depending on the means to be used for the system of the presentinvention, said means may be functionally linked by connecting each meanwith the other by means which allow data transport in between saidmeans, e.g., glass fiber cables, and other cables for high throughputdata transport. Nevertheless, wireless data transfer between the meansis also envisaged by the present invention, e.g., via LAN (Wireless LAN,W-LAN). A preferred system comprises means for determining biomarkers.Means for determining biomarkers as used herein encompass means forseparating biomarkers, such as chromatographic devices, and means formetabolite determination, such as mass spectrometry devices. Suitabledevices have been described in detail above. Preferred means forcompound separation to be used in the system of the present inventioninclude chromatographic devices, more preferably devices for liquidchromatography, HPLC, and/or gas chromatography. Preferred devices forcompound determination comprise mass spectrometry devices, morepreferably, GC-MS, LC-MS, direct infusion mass spectrometry, FT-ICR-MS,CE-MS, HPLC-MS, quadrupole mass spectrometry, sequentially coupled massspectrometry (including MS-MS or MS-MS-MS), ICP-MS, Py-MS or TOF. Theseparation and determination means are, preferably, coupled to eachother. Most preferably, LC-MS and/or GC-MS are used in the system of thepresent invention as described in detail elsewhere in the specification.Further comprised shall be means for comparing and/or analyzing theresults obtained from the means for determination of biomarkers. Themeans for comparing and/or analyzing the results may comprise at leastone databases and an implemented computer program for comparison of theresults. Preferred embodiments of the aforementioned systems and devicesare also described in detail below.

Therefore, the present invention relates to a diagnostic devicecomprising:

-   -   a) an analysing unit comprising a detector for at least one        biomarker as listed in any one of Tables 1a to c, 3, 4a to c or        6, wherein said analyzing unit is adapted for determining the        amount of the said biomarker detected by the detector, and,        operatively linked thereto;    -   b) an evaluation unit comprising a computer comprising tangibly        embedded a computer program code for carrying out a comparison        of the determined amount of the at least one biomarker and a        reference amount and a data base comprising said reference        amount as for the said biomarker whereby it will be diagnosed        whether a subject suffers from heart failure, is in need for a        therapy of heart failure or has underwent a successful therapy        of heart failure if the result of the comparison for the at        least one biomarker is essentially identical to the kind of        regulation and/or fold of regulation indicated for the        respective at least one biomarker in any one of Tables 1a to c,        3, 4a to c or 6.

In a preferred embodiment, the device comprises a further databasecomprising the kind of regulation and/or fold of regulation valuesindicated for the respective at least one biomarker in any one of Tables1a to c, 3, 4a to c or 6 and a further tangibly embedded computerprogram code for carrying out a comparison between the determined kindof regulation and/or fold of regulation values and those comprised bythe database.

Therefore, the present invention also relates to a diagnostic devicecomprising:

-   -   a) an analysing unit comprising a detector for at least one        biomarker as listed in any one of Tables 2 or 5, wherein said        analyzing unit is adapted for determining the amount of the said        biomarker detected by the detector, and, operatively linked        thereto;    -   b) an evaluation unit comprising a computer comprising tangibly        embedded a computer program code for (i) calculating a ratio of        the at least one biomarker of a second and a first sample        and (ii) carrying out a comparison of the determined ratio of        the at least one biomarker and a reference ratio and a data base        comprising said reference ratio for the said biomarker whereby        it will be diagnosed whether a subject suffers from heart        failure, is in need for a therapy of heart failure or has        underwent a successful therapy of heart failure if the result of        the comparison for the at least one biomarker is essentially        identical to the kind of regulation and/or fold of regulation        indicated for the respective at least one biomarker in any one        of Tables 2 or 5.

In a preferred embodiment, the device comprises a further databasecomprising the kind of regulation and/or fold of regulation valuesindicated for the respective at least one biomarker in any one of Tables2 or 5 and a further tangibly embedded computer program code forcarrying out a comparison between the determined kind of regulationand/or fold of regulation values and those comprised by the database.

Furthermore, the present invention relates to a data collectioncomprising characteristic values of at least one biomarker beingindicative for a medical condition or effect as set forth above (i.e.diagnosing heart failure in a subject, identifying whether a subject isin need for a therapy of heart failure or determining whether a heartfailure therapy is successful).

The term “data collection” refers to a collection of data which may bephysically and/or logically grouped together. Accordingly, the datacollection may be implemented in a single data storage medium or inphysically separated data storage media being operatively linked to eachother. Preferably, the data collection is implemented by means of adatabase. Thus, a database as used herein comprises the data collectionon a suitable storage medium. Moreover, the database, preferably,further comprises a database management system. The database managementsystem is, preferably, a network-based, hierarchical or object-orienteddatabase management system. Furthermore, the database may be a federalor integrated database. More preferably, the database will beimplemented as a distributed (federal) system, e.g. as aClient-Server-System. More preferably, the database is structured as toallow a search algorithm to compare a test data set with the data setscomprised by the data collection. Specifically, by using such analgorithm, the database can be searched for similar or identical datasets being indicative for a medical condition or effect as set forthabove (e.g. a query search). Thus, if an identical or similar data setcan be identified in the data collection, the test data set will beassociated with the said medical condition or effect. Consequently, theinformation obtained from the data collection can be used, e.g., as areference for the methods of the present invention described above. Morepreferably, the data collection comprises characteristic values of allbiomarkers comprised by any one of the groups recited above.

In light of the foregoing, the present invention encompasses a datastorage medium comprising the aforementioned data collection.

The term “data storage medium” as used herein encompasses data storagemedia which are based on single physical entities such as a CD, aCD-ROM, a hard disk, optical storage media, or a diskette. Moreover, theterm further includes data storage media consisting of physicallyseparated entities which are operatively linked to each other in amanner as to provide the aforementioned data collection, preferably, ina suitable way for a query search.

The present invention also relates to a system comprising:

-   (a) means for comparing characteristic values of the at least one    biomarker of a sample operatively linked to-   (b) a data storage medium as described above.

The term “system” as used herein relates to different means which areoperatively linked to each other. Said means may be implemented in asingle device or may be physically separated devices which areoperatively linked to each other. The means for comparing characteristicvalues of biomarkers, preferably, based on an algorithm for comparisonas mentioned before. The data storage medium, preferably, comprises theaforementioned data collection or database, wherein each of the storeddata sets being indicative for a medical condition or effect referred toabove. Thus, the system of the present invention allows identifyingwhether a test data set is comprised by the data collection stored inthe data storage medium. Consequently, the methods of the presentinvention can be implemented by the system of the present invention.

In a preferred embodiment of the system, means for determiningcharacteristic values of biomarkers of a sample are comprised. The term“means for determining characteristic values of biomarkers” preferablyrelates to the aforementioned devices for the determination ofmetabolites such as mass spectrometry devices, NMR devices or devicesfor carrying out chemical or biological assays for the biomarkers.

Moreover, the present invention relates to a diagnostic means comprisingmeans for the determination of at least one biomarker selected from anyone of the groups referred to above.

The term “diagnostic means”, preferably, relates to a diagnostic device,system or biological or chemical assay as specified elsewhere in thedescription in detail.

The expression “means for the determination of at least one biomarker”refers to devices or agents which are capable of specificallyrecognizing the biomarker. Suitable devices may be spectrometric devicessuch as mass spectrometry, NMR devices or devices for carrying outchemical or biological assays for the biomarkers. Suitable agents may becompounds which specifically detect the biomarkers. Detection as usedherein may be a two-step process, i.e. the compound may first bindspecifically to the biomarker to be detected and subsequently generate adetectable signal, e.g., fluorescent signals, chemiluminescent signals,radioactive signals and the like. For the generation of the detectablesignal further compounds may be required which are all comprised by theterm “means for determination of the at least one biomarker. Compoundswhich specifically bind to the biomarker are described elsewhere in thespecification in detail and include, preferably, enzymes, antibodies,ligands, receptors or other biological molecules or chemicals whichspecifically bind to the biomarkers.

Further, the present invention relates to a diagnostic compositioncomprising at least one biomarker selected from any one of the groupsreferred to above.

The at least one biomarker selected from any of the aforementionedgroups will serve as a biomarker, i.e. an indicator molecule for amedical condition or effect in the subject as set for the elsewhereherein. Thus, the biomarker molecules itself may serve as diagnosticcompositions, preferably, upon visualization or detection by the meansreferred to in herein. Thus, a diagnostic composition which indicatesthe presence of a biomarker according to the present invention may alsocomprise the said biomarker physically, e.g., a complex of an antibodyand the biomarker to be detected may serve as the diagnosticcomposition. Accordingly, the diagnostic composition may furthercomprise means for detection of the metabolites as specified elsewherein this description. Alternatively, if detection means such as MS or NMRbased techniques are used, the molecular species which serves as anindicator for the risk condition will be the at least one biomarkercomprised by the test sample to be investigated. Thus, the at least onebiomarker referred to in accordance with the present invention shallserve itself as a diagnostic composition due to its identification as abiomarker.

In general, the present invention contemplates the use of at least onebiomarker selected from the biomarkers in any one of Tables 1a to c, 3,4a to c, or 6 in a sample of a subject for diagnosing heart failure,and, preferably, the use of at least one biomarker selected from thebiomarkers in any one of Tables 4a to c or 6 in a sample of a subjectfor diagnosing heart failure according to NYHA class I and the use of atleast one biomarker selected from the biomarkers in any one of Tables 1ato c or 3 in a sample of a subject for diagnosing heart failureaccording to NYHA class I, II and/or III. In case of the biomarkerslisted in Tables 3 or 6, the sample shall have been obtained from thesubject under exercise. The present invention also contemplates the useof at least one biomarker selected from the any one of Tables 1a or 4ain a sample of a subject for diagnosing dilated cardiomyopathy, the useof at least one biomarker selected from the any one of Tables 1b or 4bin a sample of a subject for diagnosing ischemic cardiomyopathy and/ordilatative cardiomyopathy and the use of at least one biomarker selectedfrom the any one of Tables 1c or 4c in a sample of a subject fordiagnosing hypertrophic cardiomyopathy and/or dilatative cardiomyopathy.

Moreover, the present invention pertains to the use, in general, of aratio of at least one biomarker selected from any one of Tables 2 or 5calculated from a first and a second sample of a subject for diagnosingheart failure. Preferably, heart failure according to NYHA class I canbe diagnosed by the biomarkers of Table 5, while heart failure accordingto NYHA class I, II and/or III can be diagnosed by the biomarkers ofTable 2. It will be understood that the ratio of at least one biomarkershall have been calculated from a first and a second sample obtainedfrom the subject at rest and under exercise.

All references cited herein are herewith incorporated by reference withrespect to their disclosure content in general or with respect to thespecific disclosure contents indicated above.

The invention will now be illustrated by the following Examples whichare not intended to restrict or limit the scope of this invention.

EXAMPLES

The invention will now be illustrated by the following Examples whichare not intended to restrict or limit the scope of this invention.

Example 1 Study Design for DCMP (Dilated Cardiomyopathy) and Preparationof amples

22 male patients suffering from dilated cardiomyopathy and 19 healthymale controls were included in the study. NYHA (New York HeartAssociation) scores of the patients ranged from 1-3, and the leftventricular ejection fraction (LVEF) from 10-55%. Patients and controlswere matched for age and BMI. For all patients and controls, a bloodsample was drawn before (t0) or immediately after (t1) spiroergometerexercise testing. A third blood sample was obtained one hour afterexercise testing (t2). Plasma was prepared from all samples bycentrifugation, and samples were stored at −80° C. until measurementswere performed. Spiroergometry was performed as follows: At thebeginning, a load of 15 Watt was applied which was increased every 2minutes for additional 15 Watt. For all patients, the volume of breathper minute (VE), oxygen uptake (VO₂), carbon dioxide emission (VCO₂) aswell as the frequency of breathing was determined. A respiratory ratiowas calculated as follows: RQ=VCO₂/VO₂. Breathing equivalents werecalculated for O₂ (AÄO₂=VE/VO₂), for CO₂ (=VE/VCO₂). The volume perbreath (AZV=VE/AF) was calculated as well. Spiroergometry was aborted ifthe patient was exhausted, if cardiac arrhythmia occurred (e.g., atrialfluttering, blockade of higher order conduct ways, increased ventricularextrasystoles, permanent ventricular tachycardy), if signs of myocardialischemia could be determined clinically or by electrocardiography, orafter application of a load of 285 Watt. Duration of the spiroergometrytesting varied between 2 and 38 minutes.

Patients with apparent dilated cardiomypathy were included if theyexhibited a LVEF of <55% and symptoms according to NYHA I to III. NYHAIV patients were excluded as well as patients suffering from apolex,patients who had myocardial infarction within the last 4 month beforetesting, patients with altered medications within the last 4 weeksbefore testing as well as patients who suffered from acute or chronicinflammatory diseases and malignant tumours.

Example 2 Study Design for the Differentiation of CHF Subtypes DCMP(Dilated Cardiomyopathy), ICMP (Ischemic Cardiomyopathy) and HCMP(Hypertrophic Cardiomyopathy) from Healthy Controls

The study comprised 81 male and female DCMP-, 81 male and female ICMP-and 80 male and female HCMP patients as well as 83 male and femalehealthy controls in an age range from 35-75 and a BMI rage from 20-35kg/m2 were included. NYHA (New York Heart Association) scores of thepatients ranged from 1-3. Patients and controls were matched for age,gender and BMI. For all patients and controls, a blood sample wascollected. Plasma was prepared by centrifugation, and samples werestored at −80° C. until measurements were performed.

Three subgroups of CHF (DCMP, ICMP and HCMP) were defined on the basisof echocardiography and hemodynamic criteria:

-   -   a) Subgroup DCMP: is hemodynamically defined as a systolic pump        failure with cardiomegaly (echocardiographic enhancement of the        left ventricular end diastolic diameter >55 mm and a restricted        left ventricular ejection fraction—LVEF of <50%).    -   b) Subgroup ICMP: is hemodynamically defined as systolic pump        failure due to a coronary insufficiency (>50% coronary stenosis        and a stress inducible endocardium motion insufficiency as well        as an LVEF of <50%)    -   c) Subgroup HCMP: concentric heart hypertrophy        (echocardiography—septum >11 mm, posterior myocardial        wall >11 mm) and with a diastolic CHF (non or mildly impaired        pump function with LVEF of ≧50%).

NYHA IV patients were excluded as well as patients suffering fromapoplex, patients who had myocardial infarction within the last 4 monthsbefore testing, patients with altered medications within the last 4weeks before testing as well as patients who suffered from acute orchronic inflammatory diseases and malignant tumours.

Example 3 Determination of Metabolites

Human plasma samples were prepared and subjected to LC-MS/MS and GC-MSor SPE-LC-MS/MS (hormones) analysis as described in the following:

Proteins were separated by precipitation from blood plasma. Afteraddition of water and a mixture of ethanol and dichlormethan theremaining sample was fractioned into an aqueous, polar phase and anorganic, lipophilic phase.

For the transmethanolysis of the lipid extracts a mixture of 140 μl ofchloroform, 37 μl of hydrochloric acid (37% by weight HCl in water), 320μl of methanol and 20 μl of toluene was added to the evaporated extract.The vessel was sealed tightly and heated for 2 hours at 100° C., withshaking. The solution was subsequently evaporated to dryness. Theresidue was dried completely.

The methoximation of the carbonyl groups was carried out by reactionwith methoxyamine hydrochloride (20 mg/ml in pyridine, 100 μl for 1.5hours at 60° C.) in a tightly sealed vessel. 20 μl of a solution ofodd-numbered, straight-chain fatty acids (solution of each 0.3 mg/mL offatty acids from 7 to 25 carbon atoms and each 0.6 mg/mL of fatty acidswith 27, 29 and 31 carbon atoms in 3/7 (v/v) pyridine/toluene) wereadded as time standards. Finally, the derivatization with 100 μl ofN-methyl-N-(trimethylsilyl)-2,2,2-trifluoroacetamide (MSTFA) was carriedout for 30 minutes at 60° C., again in the tightly sealed vessel. Thefinal volume before injection into the GC was 220 μl.

For the polar phase the derivatization was performed in the followingway: The methoximation of the carbonyl groups was carried out byreaction with methoxyamine hydrochloride (20 mg/ml in pyridine, 50 μlfor 1.5 hours at 60° C.) in a tightly sealed vessel. 10 μl of a solutionof odd-numbered, straight-chain fatty acids (solution of each 0.3 mg/mLof fatty acids from 7 to 25 carbon atoms and each 0.6 mg/mL of fattyacids with 27, 29 and 31 carbon atoms in 3/7 (v/v) pyridine/toluene)were added as time standards. Finally, the derivatization with 50 μl ofN-methyl-N-(trimethylsilyl)-2,2,2-trifluoroacetamide (MSTFA) was carriedout for 30 minutes at 60° C., again in the tightly sealed vessel. Thefinal volume before injection into the GC was 110 μl.

The GC-MS systems consist of an Agilent 6890 GC coupled to an Agilent5973 MSD. The autosamplers are CompiPal or GCPaI from CTC.

For the analysis usual commercial capillary separation columns (30m×0.25 mm×0.25 μm) with different poly-methyl-siloxane stationary phasescontaining 0% up to 35% of aromatic moieties, depending on the analysedsample materials and fractions from the phase separation step, were used(for example: DB-1 ms, HP-5 ms, DB-XLB, DB-35 ms, Agilent Technologies).Up to 1 μL of the final volume was injected splitless and the oventemperature program was started at 70° C. and ended at 340° C. withdifferent heating rates depending on the sample material and fractionfrom the phase separation step in order to achieve a sufficientchromatographic separation and number of scans within each analyte peak.Furthermore RTL (Retention Time Locking, Agilent Technologies) was usedfor the analysis and usual GC-MS standard conditions, for exampleconstant flow with nominal 1 to 1.7 ml/min. and helium as the mobilephase gas, ionisation was done by electron impact with 70 eV, scanningwithin a m/z range from 15 to 600 with scan rates from 2.5 to 3scans/sec and standard tune conditions.

The HPLC-MS systems consisted of an Agilent 1100 LC system (AgilentTechnologies, Waldbronn, Germany) coupled with an API 4000 Massspectrometer (Applied Biosystem/MDS SCIEX, Toronto, Canada). HPLCanalysis was performed on commercially available reversed phaseseparation columns with C18 stationary phases (for example: GROM ODS 7pH, Thermo Betasil C18). Up to 10 μL of the final sample volume ofevaporated and reconstituted polar and lipophilic phase was injected andseparation was performed with gradient elution usingmethanol/water/formic acid or acetonitrile/water/formic acid gradientsat a flowrate of 200 μL/min.

Mass spectrometry was carried out by electrospray ionisation in positivemode for the non-polar fraction and negative mode for the polar fractionusing multiple-reaction-monitoring-(MRM)-mode and fullscan from 100-1000amu.

Steroids and their metabolites were measured by online SPE-LC-MS (Solidphase extraction-LC-MS). Catecholamines and their metabolites weremeasured by online SPE-LC-MS as described by Yamada et al. (J. Anal.Toxicol. (26), 2002, 17-22). For both catecholamines and relatedmetabolites and steroids and related metabolites, quantification wasachieved by means of stable-isotope-labelled standards, and absoluteconcentrations were calculated.

Analysis of Complex Lipids in Plasma Samples:

Total lipids were extracted from plasma by liquid/liquid extractionusing chloroform/methanol.

The lipid extracts were subsequently fractionated by normal phase liquidchromatography (NPLC) into eleven different lipid groups according toChristie (Journal of Lipid Research (26), 1985, 507-512).

The fractions were analyzed by LC-MS/MS using electrospray ionization(ESI) and atmospheric pressure chemical ionization (APCI) with detectionof specific multiple reaction monitoring (MRM) transitions forcholesterol esters (CE) sphingoymelins (SM), and ceramides (CER)respectively. Sphingosines and sphingosine-1-phosphates (SP) wereanalyzed by LC-MS/MS using electrospray ionization (ESI) with detectionof specific multiple reaction monitoring (MRM) transitions as describedby Schmidt H et.al., Prostaglandins & other Lipid Mediators 81(2006),162-170. Metabolites in tables 1a, 1b, 1c, 4a, 4b and 4c, derived fromone of these fractions include the respective abbreviation in theirname.

Eicosanoids and related were measured out of plasma by offline- andonline-SPE LC-MS/MS (Solid phase extraction-LC-MS/MS) (Masoodi M andNicolaou A: Rapid Commun Mass Spectrom. 2006; 20(20): 3023-3029.Absolute quantification was performed by means of stableisotope-labelled standards.

Example 4 Data Analysis and Statistical Evaluation

Plasma samples were analyzed in randomized analytical sequence designwith pooled samples (so called “pool”) generated from aliquots of eachsample. Following comprehensive analytical validation steps, the rawpeak data for each analyte were normalized to the median of pool peranalytical sequence to account for process variability (so called“pool-normalized ratios”). If available, absolute concentrations ofmetabolites were used for statistical analysis. In all other cases,pool-normalized ratios were used. All data were log 10-transformed toachieve normal distribution.

For the study described in Example 1, a mixed-effects model was designedcontaining the factors age (numerical), BMI (numerical), diagnosis (CHF,control-reference: control), time point (categorical—t0, t1,t2—reference: t0, before exercise) and interaction time point:diagnosis.All factors except for diagnosis were optional, only included ifpositively contributing to model quality. Proband (each patient orhealthy control) was treated as random factor. Statistical significancewas read out from p-values of t-statistics. Direction and strength ofregulation were obtained by calculation of ratios of median values forthe groups to be compared. Regulation type was determined for eachmetabolite as “up” for increased (ratios>1) within the respective group(CHF) vs. reference (healthy controls) and “down” for decreased(ratios<1) vs. reference.

In order to identify biomarkers of heart failure, the read-out fordiagnosis at the pre-exercise time point t0 was considered (table 1). Inorder to read out diagnosis effects at time points t1 and t2, additionalmodels were calculated with reference for factor time point set to t1 ort2, respectively. To find biomarkers of heart failure in patientsundergoing exercise testing, two different read-outs of themixed-effects model were analyzed. First, metabolite lists were filteredfor significance of factor diagnosis at t1 but not at t0 (see table 3).Alternatively, p-values for interaction time point:diagnosis were readout at time point t1 (reference time point t0) (see table 2).

Additional mixed-effect models were calculated to identify metabolitesindicative of mild heart failure (NYHA score I). For this purpose, themodels mentioned above were modified to contain a fixed factor NYHAscore (categorical, reference healthy control) instead of diagnosis(CHF, control-reference: control). As indicator of significance,p-values of t-statistic for NYHA score were read out at level NYHA 1(tables 4a to c). To find biomarkers of mild heart failure (NYHAscore 1) in patients undergoing exercise testing, two differentread-outs of the mixed-effects model were analyzed. First, metabolitelists were filtered for significance of factor NYHA score at level NYHA1, at t1 but not at t0 (see table 6). Second, p-values for interactiontime point:NYHA score were read out at level NYHA score 1, time point t1(reference time point t0) (see table 5).

The study described in Example 2 was analyzed by an ANOVA modelcomprising factors age, BMI, gender (including all binary interactions),diagnostic group and storage time (optional). Levels for the factordiagnostic group were CHF subtype (DCMP, ICMP, HCMP, control-reference:control). To identify a metabolic profile of early-stage DCMP, analysiswas restricted to NYHA I patients (result tables 4a to c). In this case,levels for the factor diagnostic group were DCMP NYHA I, DCMP NYHAII-III, ICMP NYHA I, ICMP NYHA II-III, HCMP NYHA I, HCMP NYHA II-III andcontrol (set as reference).

In tables 1-6, ratio of median indicates strength and direction ofregulation. Ratio of median was calculated by dividing the median ofmetabolite level in the CHF group by the median of metabolite level inthe healthy control group. For tables 2 and 5, t0-normalized data(metabolite level at time point t1 divided by metabolite level at timepoint t0) were used for calculation.

The results of the analyses are summarized in the following tables,below. The biomarkers to be determined in accordance with the methods ofthe present invention are listed in the following tables. Biomarkers notprecisely defined by their name are further characterized in table 7.

TABLE 1a Metabolites with a significant difference (p-value <0.05)between patients with CHF (dilated cardiomyopathy) and healthy controlsMetabolite_Name ratio of median regulation p-valueLysophosphatidylcholine (C18:2) 0.656 down 0.000002 Mannose 1.949 up0.000000 Hypoxanthine 2.136 up 0.000006 Phytosphingosine 0.779 down0.000010 Lignoceric acid (C24:0) 0.654 down 0.000029 Glutamate 2.027 up0.000037 2-Hydroxybutyrate 1.724 up 0.000132 Lysophosphatidylcholine(C18:0) 0.820 down 0.000213 Behenic acid (C22:0) 0.744 down 0.000224Tricosanoic acid (C23:0) 0.708 down 0.000237 Phosphatidylcholine (C18:0,C18:2) 1.028 up 0.000248 Linoleic acid (C18:cis[9,12]2) 0.733 down0.000270 Pseudouridine 1.299 up 0.000321 Phosphate, lipid fraction 0.817down 0.000333 Lysophosphatidylcholine (C18:1) 0.874 down 0.000432Lysophosphatidylcholine (C17:0) 0.770 down 0.000612 erythro-Sphingosine(*1) 0.823 down 0.000620 Glycerol phosphate, lipid fraction 0.768 down0.000628 5-O-Methylsphingosine (*1) 0.802 down 0.000766 Galactose, lipidfraction 0.775 down 0.000846 Cholesterol 0.855 down 0.000921alpha-Ketoglutarate 1.235 up 0.000944 Histidine 0.790 down 0.000945Eicosanoic acid (C20:0) 0.835 down 0.001148 3-O-Methylsphingosine (*1)0.769 down 0.001248 erythro-C16-Sphingosine 0.827 down 0.001492 Uricacid 1.429 up 0.001696 Cholesterol No 02 0.821 down 0.004244 Urea 1.243up 0.005073 Adrenaline (Epinephrine) 1.926 up 0.006118 Aspartate 1.120up 0.006265 Normetanephrine 1.262 up 0.006469 Pentadecanol 0.583 down0.006875 myo-Inositol, lipid fraction 0.775 down 0.007379Dehydroepiandrosterone sulfate 0.594 down 0.007754 Phosphatidylcholine(C16:1, C18:2) 0.883 down 0.008776 Sphingomyelin (d18:1, C24:0) 0.943down 0.011533 Threonine 0.855 down 0.012287 myo-Inositol-2-phosphate,lipid fraction (myo- 0.635 down 0.012637 Inositolphospholipids) Myristicacid (C14:0) 0.572 down 0.015030 Homovanillic acid (HVA) 1.292 up0.015937 Arginine 0.844 down 0.016192 Glutamine 0.850 down 0.016336Elaidic acid (C18:trans[9]1) 1.267 up 0.0174104-Hydroxy-3-methoxyphenylglycol (HMPG) 1.128 up 0.019069 Cystine 1.105up 0.020208 4-Hydroxy-3-methoxymandelic acid 1.179 up 0.020480Zeaxanthin 0.699 down 0.021888 Glucose 1.215 up 0.023219 Stearic acid(C18:0) 0.918 down 0.023703 Cortisol 1.345 up 0.025615 3-Methoxytyrosine1.209 up 0.026958 5-Hydroxy-3-indoleacetic acid (5-HIAA) 1.255 up0.027467 Lysophosphatidylcholine (C20:4) 0.944 down 0.029167 Creatinine1.208 up 0.031253 Heptadecanoic acid (C17:0) 0.828 down 0.032349 Proline0.818 down 0.033617 Erythrol 1.224 up 0.035087 Nervonic acid(C24:cis[15]1) 0.879 down 0.035240 Coenzyme Q10 1.060 up 0.036613Coenzyme Q9 0.774 down 0.040228 Phosphatidylcholine (C18:0, C18:1) 0.966down 0.044253 Cryptoxanthin 0.464 down 0.047617 1,5-Anhydrosorbitol0.808 down 0.047807 SM_Sphingomyelin (d17:1, C24:0) 0.7142 down 2.8E−13SM_Sphingomyelin (d17:1, C22:0) 0.7423 down 9.8E−12 SM_Sphingomyelin(d17:1, C23:0) 0.6392 down 1.4E−11 CE_Cholesterylester C15:0 0.6745 down8.8E−11 Cholesterylester C18:2 0.7013 down 2.1E−10 SM_Sphingomyelin(d16:1, C23:0) 0.7103 down 2.7E−10 Isocitrate 1.2983 up 4.6E−101-Hydroxy-2-amino-(cis,trans)-3,5-octadecadiene 0.738 down 1.2E−09 (fromsphingolipids) Noradrenaline (Norepinephrine) 1.5067 up 4.9E−09SM_Sphingomyelin (d16:1, C22:0) 0.7499 down 8.7E−09 SM_Sphingomyelin(d16:1, C24:0) 0.6773 down 1.1E−08 Maltose 1.8136 up 1.9E−08SM_Sphingomyelin (d18:2, C23:0) 0.8134 down 2.7E−08 SM_Sphingomyelin(d17:1, C20:0) 0.7884 down   3E−08 SM_Sphingomyelin (d17:1, C16:0)0.8169 down 1.6E−07 SM_Sphingomyelin (d18:1, C14:0) 0.8274 down 2.5E−07CE_Cholesterylester C14:0 0.7641 down 5.2E−07 Sphingomyelin (d18:1,C23:0) 0.8793 down 6.2E−07 CER_Ceramide (d17:1, C24:0) 0.7452 down1.7E−06 SM_Sphingomyelin (d18:2, C24:0) 0.834 down 2.3E−06 Uridine0.7617 down 3.4E−06 CER_Ceramide (d18:2, C14:0) 0.7732 down 6.9E−06CER_Ceramide (d17:1, C23:0) 0.7443 down   9E−06 SM_Sphingomyelin (d16:1,C20:0) 0.8091 down   1E−05 SM_Sphingomyelin (d17:1, C24:1) 0.8482 down2.2E−05 SM_Sphingomyelin (d17:1, C18:0) 0.8393 down   3E−05CE_Cholesterylester C22:6 0.7561 down 3.3E−05 SM_Sphingomyelin (d16:1,C22:1) 0.8034 down 3.6E−05 myo-Inositol 1.16 up 4.6E−05 CER_Ceramide(d16:1, C24:0) 0.762 down 6.7E−05 beta-Carotene 0.7066 down 8.1E−05SM_Sphingomyelin (d16:1, C24:1) 0.8446 down 0.00011 Ornithine 1.1516 up0.00012 SM_Sphingomyelin (d18:2, C22:0) 0.8501 down 0.00013Cholesta-2,4,6-triene 0.8494 down 0.00016 TAG (C16:0, C18:2) 1.3317 up0.00017 CE_Cholesterylester C16:2 0.7746 down 0.00017CE_Cholesterylester C20:5 0.7085 down 0.00018 Sorbitol 1.5523 up 0.00019SM_Sphingomyelin (d18:2, C23:1) 0.8561 down 0.00021 Isopalmitic acid(C16:0) 0.7684 down 0.00022 Sarcosine 1.1039 up 0.00024Phosphatidylcholine (C18:2, C20:4) 0.9367 down 0.00025 CER_Ceramide(d18:1, C14:0) 0.8316 down 0.00026 SM_Sphingomyelin (d16:1, C18:1)0.8335 down 0.00031 Sphingosine-1-phosphate (d17:1) 0.8268 down 0.00032TAG (C16:0, C18:1, C18:2) 1.4134 up 0.00034 SM_Sphingomyelin (d16:1,C21:0) 0.8077 down 0.00038 CER_Ceramide (d16:1, C23:0) 0.7763 down0.00038 Docosahexaenoic acid (C22:cis[4,7,10,13,16,19]6) 0.7778 down0.00044 TAG (C18:1, C18:2) 1.3426 up 0.00053 Tyrosine 1.1292 up 0.00057Testosterone 0.7956 down 0.00059 threo-Sphingosine (*1) 0.8766 down0.00078 Phenylalanine 1.0929 up 0.00081 CE_Cholesterylester C14:1 0.68down 0.00082 Cholesta-2,4-dien 0.8533 down 0.00096 SM_Sphingomyelin(d16:1, C16:0) 0.8766 down 0.00114 Malate 1.1907 up 0.00116SM_Sphingomyelin (d18:1, C22:0) 0.8379 down 0.00119 CE_CholesterylesterC16:3 0.7918 down 0.00122 5-Oxoproline 1.0814 up 0.00123CE_Cholesterylester C22:5 0.8603 down 0.00125 SM_Sphingomyelin (d18:1,C23:1) 0.8878 down 0.00132 Docosapentaenoic acid(C22:cis[7,10,13,16,19]5) 0.8085 down 0.00165 CER_Ceramide (d17:1,C16:0) 0.8577 down 0.00176 Taurine 1.1928 up 0.00178 Phosphatidylcholine(C16:0, C20:5) 0.9159 down 0.00195 SM_Sphingomyelin (d18:2, C14:0) 0.871down 0.00207 Cholesterylester C18:1 0.8256 down 0.00219 CER_Ceramide(d17:1, C22:0) 0.8324 down 0.00247 CE_Cholesterylester C18:3 0.7933 down0.00311 CER_Ceramide (d18:1, C18:0) 1.1562 up 0.00456 SM_Sphingomyelin(d18:2, C21:0) 0.8893 down 0.00466 CE_Cholesterylester C18:4 0.7197 down0.00569 SM_Sphingomyelin (d16:1, C18:0) 0.8762 down 0.0057Glycerol-3-phosphate, polar fraction 1.159 up 0.00613 CholesterylesterC16:0 0.8225 down 0.00685 Eicosapentaenoic acid (C20:cis[5,8,11,14,17]5)0.7853 down 0.00809 CE_Cholesterylester C12:0 0.7224 down 0.00887trans-4-Hydroxyproline 1.2178 up 0.0089 SM_Sphingomyelin (d18:1, C21:0)0.9157 down 0.00945 CER_Ceramide (d18:2, C23:0) 0.869 down 0.00948 TAG(C16:0, C16:1) 1.2811 up 0.01131 Glycerol, lipid fraction 1.2809 up0.01216 CER_Ceramide (d16:1, C16:0) 0.8776 down 0.0122 Cysteine 1.0714up 0.01409 Phosphatidylcholine (C16:0, C20:4) 0.991 down 0.015718-Hydroxyeicosatetraenoic acid 1.2207 up 0.01617(C20:trans[5]cis[9,11,14]4) (8-HETE) Hippuric acid 0.7043 down 0.01627Sphingosine (d18:1) 1.264 up 0.01632 SM_Sphingomyelin (d18:2, C18:1)0.9068 down 0.01633 Hexadecanol 1.1092 up 0.01765 14-Methylhexadecanoicacid 0.8393 down 0.01844 CER_Ceramide (d16:1, C22:0) 0.8608 down 0.02052CER_Ceramide (d18:2, C24:0) 0.8903 down 0.02079 SM_Sphingomyelin (d18:2,C24:2) 0.9157 down 0.02116 Creatine 1.1628 up 0.02211 Eicosenoic acid(C20:cis[11]1) 1.1674 up 0.02337 14,15-Dihydroxyeicosatrienoic acid1.1603 up 0.0238 (C20:cis[5,8,11]3) Sphinganine (d18:0) 1.2016 up0.02412 CER_Ceramide (d18:1, C23:0) 0.8973 down 0.02646 CER_Ceramide(d17:1, C20:0) 0.876 down 0.02705 CER_Ceramide (d18:1, C24:0) 0.8982down 0.02746 Fumarate 1.051 up 0.03023 SM_Sphingomyelin (d18:2, C20:0)0.9289 down 0.03273 conjugated Linoleic acid (C18:trans[9,11]2) 0.8624down 0.03361 13-Hydroxyoctadecadienoic acid (13-HODE) 1.1549 up 0.03371(C18:cis[9]trans[11]2) Campesterol 0.8211 down 0.035893,4-Dihydroxyphenylalanine (DOPA) 1.0983 up 0.03675 TAG (C18:2, C18:2)1.2038 up 0.03696 Phosphatidylcholine No 02 0.9467 down 0.03922Glucose-1-phosphate 1.089 up 0.03978 CER_Ceramide (d17:1, C24:1) 0.8986down 0.04172 Lactaldehyde 1.0876 up 0.04225 Methionine 1.0698 up 0.04311Lysophosphatidylethanolamine (C22:5) 0.9229 down 0.04472 scyllo-Inositol1.1685 up 0.04903 CER_Ceramide (d16:1, C21:0) 0.8656 down 0.04997 (*1):free and from sphingolipids

TABLE 1b Metabolites of table 1a which additionally showed a significantdifference (p-value <0.1) between ICMP patients and healthy controlsratio of Metabolite_Name median regulation p-value CholesterylesterC18:2 0.6066 down 3.17E−17 SM_Sphingomyelin (d18:1, C14:0) 0.7751 down3.88E−11 SM_Sphingomyelin (d18:2, C23:0) 0.7837 down 3.14E−10SM_Sphingomyelin (d17:1, C23:0) 0.661 down 1.21E−09 Tricosanoic acid(C23:0) 0.7527 down 2.78E−09 CE_Cholesterylester C15:0 0.6948 down5.44E−09 SM_Sphingomyelin (d17:1, C24:0) 0.7656 down 1.24E−081-Hydroxy-2-amino-(cis,trans)-3,5-octadecadiene (from 0.7463 down1.33E−08 sphingolipids) Sorbitol 1.9715 up 3.76E−08 SM_Sphingomyelin(d17:1, C16:0) 0.8059 down 6.53E−08 SM_Sphingomyelin (d16:1, C23:0)0.7416 down 7.29E−08 beta-Carotene 0.6178 down 1.71E−07 Glutamate 1.4858up 2.7E−07 CE_Cholesterylester C14:0 0.7622 down 2.73E−07SM_Sphingomyelin (d18:2, C23:1) 0.8017 down 4.36E−07 CholesterylesterC18:1 0.7308 down 4.92E−07 SM_Sphingomyelin (d18:2, C24:0) 0.82 down6.35E−07 SM_Sphingomyelin (d17:1, C22:0) 0.8018 down  6.9E−07SM_Sphingomyelin (d18:2, C24:2) 0.82 down 7.56E−07 Lignoceric acid(C24:0) 0.7793 down 8.82E−07 TAG (C16:0, C18:2) 1.4494 up 9.3E−07threo-Sphingosine (*1) 0.8271 down 1.11E−06 SM_Sphingomyelin (d16:1,C24:0) 0.7192 down  2.4E−06 Sphingomyelin (d18:1, C23:0) 0.8821 down2.52E−06 Phosphatidylcholine (C16:0, C20:4) 0.9828 down 2.97E−06Lysophosphatidylcholine (C17:0) 0.8091 down 3.34E−06 Cholesterol, total0.8639 down 3.68E−06 SP_Sphingosine-1-phosphate (d17:1) 0.7871 down4.86E−06 TAG (C16:0, C18:1, C18:2) 1.5361 up 7.11E−06 Glucose 1.1273 up8.77E−06 SM_Sphingomyelin (d17:1, C24:1) 0.8464 down 1.25E−05 TAG(C18:1, C18:2) 1.439 up 1.53E−05 Isocitrate 1.2014 up  1.7E−05Phosphatidylcholine (C18:0, C18:2) 1.0183 up 2.19E−05 Zeaxanthin 0.7372down 2.46E−05 CER_Ceramide (d18:1, C18:0) 1.2527 up 2.54E−05 Cysteine1.1313 up 2.62E−05 SM_Sphingomyelin (d18:1, C23:1) 0.8504 down 2.65E−05Behenic acid (C22:0) 0.839 down  2.7E−05 Maltose 1.5712 up 2.99E−05 Uricacid 1.1916 up 2.99E−05 erythro-C16-Sphingosine 0.7823 down 3.62E−05SM_Sphingomyelin (d18:2, C14:0) 0.8257 down 4.08E−05 Cholesta-2,4-dien0.8257 down 5.49E−05 Glucose-1-phosphate 1.1806 up 5.61E−055-O-Methylsphingosine (*1) 0.827 down 6.28E−05 Glycerol, lipid fraction1.4758 up   7E−05 Pseudouridine 1.1483 up 7.79E−05 TAG (C16:0, C16:1)1.4548 up 0.000109 SM_Sphingomyelin (d18:2, C22:0) 0.8469 down 0.00015Cholesta-2,4,6-triene 0.8518 down 0.000167 SM_Sphingomyelin (d16:1,C22:0) 0.8256 down 0.00017 SM_Sphingomyelin (d16:1, C24:1) 0.845 down0.000187 erythro-Sphingosine (*1) 0.8619 down 0.000211 Cystine 1.2256 up0.00026 Linoleic acid (C18:cis[9,12]2) 0.8234 down 0.0002763-O-Methylsphingosine (*1) 0.839 down 0.000315 Taurine 1.2195 up0.000362 CER_Ceramide (d18:1, C14:0) 0.8309 down 0.000397Dehydroepiandrosterone sulfate 0.6197 down 0.000427Lysophosphatidylcholine (C18:2) 0.8578 down 0.00048514,15-Dihydroxyeicosatrienoic acid (C20:cis[5,8,11]3) 1.2659 up 0.000573CER_Ceramide (d17:1, C23:0) 0.7911 down 0.000631 TAG (C18:2, C18:2)1.3485 up 0.000677 SM_Sphingomyelin (d16:1, C16:0) 0.8677 down 0.000709Erythrol 1.1759 up 0.000711 CE_Cholesterylester C12:0 0.6467 down0.000734 SM_Sphingomyelin (d16:1, C22:1) 0.8327 down 0.000787Phytosphingosine, total 0.8621 down 0.000895 alpha-Ketoglutarate 1.1818up 0.000916 8-Hydroxyeicosatetraenoic acid (C20:trans[5]cis[9,11,14]4)1.3254 up 0.001168 (8-HETE) CER_Ceramide (d17:1, C24:0) 0.8152 down0.001205 Cholesterylester C16:0 0.788 down 0.00143 CE_CholesterylesterC14:1 0.7029 down 0.001854 SM_Sphingomyelin (d18:1, C22:0) 0.8429 down0.002434 SM_Sphingomyelin (d18:2, C21:0) 0.8781 down 0.002466 Eicosenoicacid (C20:cis[11]1) 1.2263 up 0.002476 Sarcosine 1.0878 up 0.002491Adrenaline (Epinephrine) 1.4435 up 0.002549 Galactose, lipid fraction0.8964 down 0.002702 SM_Sphingomyelin (d17:1, C20:0) 0.8783 down0.002949 Isoleucine 1.1085 up 0.00385 Isopalmitic acid (C16:0) 0.8172down 0.003877 CER_Ceramide (d18:2, C14:0) 0.8446 down 0.004044CE_Cholesterylester C16:2 0.8272 down 0.004416 Normetanephrine 1.2896 up0.004728 trans-4-Hydroxyproline 1.2407 up 0.0057014-Hydroxy-3-methoxymandelic acid 1.6034 up 0.005745 Mannose 1.1511 up0.006205 CE_Cholesterylester C22:5 0.8782 down 0.006918 5-Oxoproline1.0658 up 0.007306 myo-Inositol 1.1023 up 0.009187 CE_CholesterylesterC22:6 0.8366 down 0.009822 SM_Sphingomyelin (d16:1, C21:0) 0.8596 down0.010056 CER_Ceramide (d16:1, C23:0) 0.8277 down 0.010099Lysophosphatidylcholine (C18:0) 0.9017 down 0.011903 Ornithine 1.0943 up0.012027 Noradrenaline (Norepinephrine) 1.194 up 0.01265SM_Sphingomyelin (d16:1, C18:1) 0.8798 down 0.013795 3-Methoxytyrosine1.1696 up 0.016194 Cholestenol No 02 0.9013 down 0.016563CE_Cholesterylester C18:3 0.8332 down 0.01764 CER_Ceramide (d16:1,C24:0) 0.8487 down 0.019382 Sphingomyelin (d18:1, C24:0) 0.9423 down0.020541 Testosterone 0.8537 down 0.020931 5-Hydroxy-3-indoleacetic acid(5-HIAA) 1.1514 up 0.021745 CER_Ceramide (d18:2, C23:0) 0.8822 down0.025435 SM_Sphingomyelin (d18:1, C21:0) 0.925 down 0.026263 Nervonicacid (C24:cis[15]1) 0.9114 down 0.026336 Phenylalanine 1.0625 up 0.0265Phosphatidylcholine (C16:1, C18:2) 0.9229 down 0.030568 SM_Sphingomyelin(d18:2, C18:1) 0.9133 down 0.0313 CER_Ceramide (d17:1, C16:0) 0.8986down 0.035021 Cryptoxanthin 0.8091 down 0.036128 Fumarate 1.0483 up0.036755 Tyrosine 1.0777 up 0.038994 CE_Cholesterylester C20:5 0.8236down 0.039914 CE_Cholesterylester C18:4 0.7902 down 0.043667 Malate1.1101 up 0.046935 SM_Sphingomyelin (d16:1, C20:0) 0.9095 down 0.053287CER_Ceramide (d17:1, C22:0) 0.8882 down 0.057993 Glycerol-3-phosphate,polar fraction 1.1093 up 0.061765 Uridine 0.8946 down 0.062565SM_Sphingomyelin (d17:1, C18:0) 0.9258 down 0.072709 Hippuric acid0.7791 down 0.081397 CER_Ceramide (d18:1, C23:0) 0.9177 down 0.089112Phosphate, lipid fraction 0.9505 down 0.097734 (*1): free and fromsphingolipids

TABLE 1c Metabolites of Table 1a which additionally showed a significantdifference (p-value <0.1) between HCMP patients and healthy controlsratio of Metabolite_Name median regulation p-value Maltose 2.1427 up5.39E−11 Cholesterylester C18:2 0.7523 down 1.99E−06 CholesterylesterC18:1 0.7715 down 5.23E−05 Taurine 1.2525 up 9.72E−05 TAG (C16:0, C18:2)1.2934 up 0.00091 Uric acid 1.1564 up 0.000939 TAG (C18:1, C18:2) 1.3302up 0.00099 Glycerol, lipid fraction 1.3816 up 0.001367 TAG (C16:0,C18:1, C18:2) 1.3509 up 0.002192 CE_Cholesterylester C15:0 0.8215 down0.002242 SP_Sphingosine-1-phosphate (d17:1) 0.8497 down 0.002442SP_Sphinganine (d18:0) 1.2867 up 0.002474 SP_Sphingosine (d18:1) 1.3486up 0.002704 Sarcosine 1.0901 up 0.003105 beta-Carotene 0.7568 down0.003481 Cysteine 1.0924 up 0.003905 Tricosanoic acid (C23:0) 0.8682down 0.004041 TAG (C16:0, C16:1) 1.3303 up 0.004263 Eicosenoic acid(C20:cis[11]1) 1.2145 up 0.005339 Isoleucine 1.1098 up 0.005399Sphingomyelin (d18:1, C23:0) 0.926 down 0.005483 SM_Sphingomyelin(d18:2, C23:0) 0.897 down 0.006161 Noradrenaline (Norepinephrine) 1.2232up 0.006926 Lysophosphatidylcholine (C17:0) 0.8834 down 0.008806Testosterone 0.8292 down 0.009379 TAG (C18:2, C18:2) 1.2656 up 0.009482Isocitrate 1.1189 up 0.011414 SM_Sphingomyelin (d17:1, C24:0) 0.885 down0.011423 SM_Sphingomyelin (d17:1, C23:0) 0.8387 down 0.011783 Zeaxanthin0.8283 down 0.012366 SM_Sphingomyelin (d16:1, C23:0) 0.869 down 0.014315Cryptoxanthin 0.7778 down 0.018169 Erythrol 1.121 up 0.023299CER_Ceramide (d17:1, C23:0) 0.8563 down 0.030171 Cholesterylester C16:00.8446 down 0.030834 SM_Sphingomyelin (d17:1, C22:0) 0.9062 down0.032352 SM_Sphingomyelin (d18:1, C21:0) 0.9242 down 0.032429SM_Sphingomyelin (d16:1, C21:0) 0.8795 down 0.034803 Glucose 1.0597 up0.035437 Glutamate 1.1813 up 0.036213 Fumarate 1.0499 up 0.03758SM_Sphingomyelin (d17:1, C20:0) 0.9101 down 0.039401 CE_CholesterylesterC14:0 0.8974 down 0.044159 Cystine 1.1237 up 0.0448818-Hydroxyeicosatetraenoic acid 1.1957 up 0.047092(C20:trans[5]cis[9,11,14]4) (8-HETE)1-Hydroxy-2-amino-(cis,trans)-3,5-octadecadiene (from 0.9003 down0.047207 sphingolipids) Uridine 0.8827 down 0.047309 Sorbitol 1.2852 up0.048213 SM_Sphingomyelin (d18:1, C14:0) 0.9258 down 0.049457 Elaidicacid (C18:trans[9]1) 1.6069 up 0.05134 SM_Sphingomyelin (d18:2, C21:0)0.9165 down 0.052457 Aspartate 1.0842 up 0.056222 Coenzyme Q10 1.1425 up0.068217 CER_Ceramide (d18:1, C18:0) 1.1056 up 0.070545 SM_Sphingomyelin(d17:1, C16:0) 0.9289 down 0.07279 SM_Sphingomyelin (d18:2, C23:1)0.9224 down 0.073683 Lactaldehyde 1.0822 up 0.078804 Pseudouridine1.0653 up 0.082343 Hippuric acid 0.7733 down 0.083253 SM_Sphingomyelin(d18:1, C23:1) 0.9341 down 0.088944 CER_Ceramide (d17:1, C24:0) 0.8949down 0.091594 Glucose-1-phosphate 1.0739 up 0.091687 SM_Sphingomyelin(d18:2, C24:0) 0.933 down 0.092261

TABLE 2 Metabolites with a significant difference (p-value <0.05) inexercise-induced change between CHF and control ratio of Metabolitemedian regulation p-value Glutamate 0.724 down 0.000274 Hypoxanthine0.448 down 0.000276 Adrenaline (Epinephrine) 0.439 down 0.001258 Lactate0.612 down 0.005556 Indole-3-lactic acid 1.198 up 0.007027 Threonic acid1.160 up 0.018026 Cholestenol No 02 0.906 down 0.022576alpha-Tocotrienol 1.206 up 0.028952 Coenzyme Q9 1.166 up 0.029375Histidine 1.083 up 0.039156 Phosphatidylcholine (C18:0, C20:4) 1.008 up0.039198 Lysophosphatidylcholine (C18:1) 1.027 up 0.040233

TABLE 3 Metabolites with a significant difference (p-value <0.05)between patients with CHF and healthy controls at the peak of exercise(t1) but not at rest (t0) Time point t0 t0 t1 ratio of regu- t0 ratio oft1 t1 Metabolite median lation p-value median regulation p-value Lactate1.149 up 0.161549 0.705 down 0.015456 Citrate 1.118 up 0.256634 1.132 up0.040482

TABLE 4a Metabolites with a significant difference (p-value <0.05)between patients with CHF (dilated cardiomyopathy) with NYHA score 1 andhealthy controls ratio of Metabolite median regulation p-value Mannose2.168 up 0.000025 Lysophosphatidylcholine (C18:2) 0.699 down 0.000748Adrenaline (Epinephrine) 2.411 up 0.004448 Hypoxanthine 1.779 up0.004996 Phosphatidylcholine (C18:0, C18:2) 1.022 up 0.012486 Glucose1.271 up 0.014916 Phosphate (inorganic and from 0.793 down 0.015030organic phosphates) Cortisol 1.340 up 0.017261 Phosphatidylcholine(C18:0, C22:6) 1.239 up 0.017614 2-Hydroxybutyrate 1.810 up 0.019583Corticosterone 1.293 up 0.019642 Androstenedione 1.785 up 0.035365Glutamate 1.333 up 0.039299 Pentadecanol 0.581 down 0.044212 Maltose1.7858 up 8.3846E−06 CE_Cholesterylester C15:0 0.7215 down  1.073E−05Cholesterylester C18:2 0.7456 down 1.7406E−05 SM_Sphingomyelin (d17:1,C24:0) 0.7957 down 2.6209E−05 Noradrenaline (Norepinephrine) 1.4153 up5.5355E−05 myo-Inositol 1.1987 up  6.44E−05 SM_Sphingomyelin (d17:1,C23:0) 0.731 down 8.1995E−05 SM_Sphingomyelin (d17:1, C22:0) 0.8196 down0.00013927 Sorbitol 1.7458 up 0.00014037 Normetanephrine 1.5039 up0.0001699 Isocitrate 1.2084 up 0.00019135 SM_Sphingomyelin (d18:1,C23:0) 0.8716 down 0.00026783 Ornithine 1.1704 up 0.00037428 Erythrol1.2249 up 0.00040476 Sarcosine 1.1251 up 0.00042563 Cystine 1.2636 up0.00044298 Testosterone 0.7586 down 0.00086625 CE_Cholesterylester C14:00.8093 down 0.0008742 Uridine 0.7862 down 0.00092815 SM_Sphingomyelin(d18:1, C14:0) 0.8622 down 0.00104019 Lignoceric acid (C24:0) 0.8223down 0.00134372 Tricosanoic acid (C23:0) 0.8376 down 0.001394311-Hydroxy-2-amino-(cis,trans)-3,5- 0.8262 down 0.00145507 octadecadiene(from sphingolipids) SM_Sphingomyelin (d16:1, C24:0) 0.7694 down0.00146283 Urea 1.2149 up 0.0015119 beta-Carotene 0.7083 down 0.00164813Tyrosine 1.1473 up 0.001792 Behenic acid (C22:0) 0.8547 down 0.00192144alpha-Ketoglutarate 1.218 up 0.00195906 SM_Sphingomyelin (d16:1, C23:0)0.8262 down 0.00281307 Taurine 1.2111 up 0.00288466 SM_Sphingomyelin(d18:1, C24:0) 0.8827 down 0.0032925 3-Methoxytyrosine 1.259 up0.00371589 Lysophosphatidylcholine (C17:0) 0.8552 down 0.00392246SM_Sphingomyelin (d18:2, C23:0) 0.8797 down 0.00428746 CER_Ceramide(d18:2, C14:0) 0.8188 down 0.00445012 SM_Sphingomyelin (d17:1, C16:0)0.8763 down 0.00489531 Cholesta-2,4,6-triene 0.8657 down 0.00545382SM_Sphingomyelin (d18:2, C24:0) 0.8781 down 0.00576839 Phenylalanine1.0947 up 0.00620035 Cysteine 1.1 up 0.00624402 SM_Sphingomyelin (d16:1,C22:0) 0.8502 down 0.00665211 Uric acid 1.1441 up 0.00696304CER_Ceramide (d17:1, C24:0) 0.8237 down 0.00931215 Glucose-1-phosphate1.1388 up 0.00940813 CE_Cholesterylester C22:5 0.8609 down 0.0095955CE_Cholesterylester C16:2 0.8095 down 0.00966362 Dehydroepiandrosteronesulfate 0.6524 down 0.00995067 Glycerol-3-phosphate, polar fraction1.1886 up 0.00997307 Isoleucine 1.1158 up 0.0102759 SM_Sphingomyelin(d17:1, C20:0) 0.8765 down 0.01056663 CER_Ceramide (d18:1, C14:0) 0.852down 0.01059946 Cholesterol, total 0.9069 down 0.01060847SM_Sphingomyelin (d18:1, C22:0) 0.8438 down 0.01179659 Linoleic acid(C18:cis[9,12]2) 0.8487 down 0.01208761 threo-Sphingosine (*1) 0.8906down 0.01352672 SM_Sphingomyelin (d17:1, C24:1) 0.9005 down 0.01479843CE_Cholesterylester C16:3 0.8114 down 0.01621643 CE_CholesterylesterC14:1 0.7197 down 0.01779781 Cholesterylester C18:1 0.837 down0.01841802 scyllo-Inositol 1.2605 up 0.02009089 CE_CholesterylesterC22:6 0.8245 down 0.02009893 Pseudouridine 1.0972 up 0.02576962CER_Ceramide (d17:1, C23:0) 0.8359 down 0.02705684erythro-C16-Sphingosine 0.8592 down 0.02915249 Eicosenoic acid(C20:cis[11]1) 1.1968 up 0.02965701 SP_Sphinganine (d18:0) 1.2368 up0.03058449 Isopalmitic acid (C16:0) 0.8326 down 0.03139525Cholesta-2,4-dien 0.8837 down 0.03222468 Lysophosphatidylcholine (C18:0)0.8999 down 0.03342501 Phosphatidylcholine (C16:1, C18:2) 0.9093 down0.03389605 Cholesterylester C16:0 0.8258 down 0.03509819 TAG (C16:0,C18:2) 1.2113 up 0.03532712 SM_Sphingomyelin (d18:2, C22:0) 0.8964 down0.03540009 CER_Ceramide (d17:1, C16:0) 0.8814 down 0.03839909 Glycerol,lipid fraction 1.2796 up 0.03879761 CE_Cholesterylester C18:3 0.8253down 0.04166858 5-Oxoproline 1.0601 up 0.04385594 CE_CholesterylesterC22:4 0.8749 down 0.04444786 Serine, lipid fraction 1.2253 up 0.0468455-O-Methylsphingosine (*1) 0.8943 down 0.04788647 TAG (C16:0, C18:1,C18:2) 1.2557 up 0.04838256 SP_Sphingosine (d18:1) 1.2602 up 0.04924965(*1): free and from sphingolipids

TABLE 4b Metabolites of Table 4a which additionally showed a significantdifference (p-value <0.1) between ICMP patients with NYHA score 1 andhealthy controls ratio of Metabolite_Name median regulation p-valueCholesterylester C18:2 0.6118 down 1.7191E−12 SM_Sphingomyelin (d18:1,C14:0) 0.7778 down 3.7018E−08 Sorbitol 2.0982 up 4.3743E−07SM_Sphingomyelin (d18:2, C23:0) 0.8125 down 4.0589E−06 SM_Sphingomyelin(d17:1, C23:0) 0.7067 down 1.1938E−05 CE_Cholesterylester C15:0 0.7269down  1.472E−05 SM_Sphingomyelin (d18:1, C23:0) 0.8512 down 1.8295E−05TAG (C16:0, C18:2) 1.453 up 1.8737E−05 Cholesterylester C18:1 0.7343down  1.939E−05 Tricosanoic acid (C23:0) 0.7919 down 2.5541E−051-Hydroxy-2-amino-(cis,trans)-3,5- 0.7813 down 3.8025E−05 octadecadiene(from sphingolipids) Cholesterol, total 0.8603 down 3.8932E−05 TAG(C16:0, C18:1, C18:2) 1.572 up 4.2286E−05 SM_Sphingomyelin (d17:1,C16:0) 0.8268 down 5.0421E−05 CE_Cholesterylester C14:0 0.785 down6.3189E−05 beta-Carotene 0.6577 down 0.00012609 threo-Sphingosine (*1)0.8433 down 0.00014084 Cholesta-2,4-dien 0.8112 down 0.00014837Lysophosphatidylcholine (C17:0) 0.8168 down 0.00018589 Glucose 1.1224 up0.00021581 Glutamate 1.3974 up 0.00024219 SM_Sphingomyelin (d17:1,C24:0) 0.8216 down 0.00026196 Lignoceric acid (C24:0) 0.8114 down0.00031083 SM_Sphingomyelin (d16:1, C23:0) 0.7977 down 0.00038589Phosphatidylcholine (C18:0, C18:2) 1.0177 up 0.00040589 SM_Sphingomyelin(d18:2, C24:0) 0.8492 down 0.00049962 5-O-Methylsphingosine (*1) 0.8249down 0.0006501 SM_Sphingomyelin (d17:1, C22:0) 0.8428 down 0.00094804Cystine 1.2438 up 0.00096287 Taurine 1.2299 up 0.00120903Glucose-1-phosphate 1.1646 up 0.00135235 SM_Sphingomyelin (d17:1, C24:1)0.8718 down 0.00137508 Glycerol, lipid fraction 1.4351 up 0.00147588Behenic acid (C22:0) 0.8594 down 0.00159109 SM_Sphingomyelin (d16:1,C24:0) 0.7739 down 0.00173184 Isocitrate 1.1685 up 0.00194376 Cysteine1.1133 up 0.0019666 3-Methoxytyrosine 1.2542 up 0.00284987 CER_Ceramide(d18:1, C14:0) 0.8291 down 0.00290481 erythro-C16-Sphingosine 0.8147down 0.00311066 Linoleic acid (C18:cis[9,12]2) 0.8385 down 0.00451392Maltose 1.4331 up 0.00496723 Adrenaline (Epinephrine) 1.5012 up0.00542671 SM_Sphingomyelin (d18:2, C22:0) 0.8703 down 0.00727388Lysophosphatidylcholine (C18:2) 0.8695 down 0.00744797 Normetanephrine1.3345 up 0.00759363 SM_Sphingomyelin (d18:1, C24:0) 0.8937 down0.0076034 Cholesterylester C16:0 0.7884 down 0.00805685 Eicosenoic acid(C20:cis[11]1) 1.2302 up 0.00826261 Cholesta-2,4,6-triene 0.8784 down0.00837711 CE_Cholesterylester C22:5 0.8605 down 0.00891577Dehydroepiandrosterone sulfate 0.6661 down 0.00966052 Pseudouridine1.1119 up 0.01037548 CE_Cholesterylester C22:4 0.8457 down 0.01129319CE_Cholesterylester C14:1 0.7165 down 0.01133041 Lysophosphatidylcholine(C18:0) 0.8831 down 0.01177707 Uric acid 1.1327 up 0.01187686SM_Sphingomyelin (d18:1, C22:0) 0.8458 down 0.01247557 Testosterone0.8204 down 0.01585512 CER_Ceramide (d17:1, C23:0) 0.8278 down0.02004195 SM_Sphingomyelin (d16:1, C22:0) 0.875 down 0.0242648Noradrenaline (Norepinephrine) 1.2117 up 0.02471946 CE_CholesterylesterC16:2 0.8476 down 0.03239574 5-Oxoproline 1.0599 up 0.03394216alpha-Ketoglutarate 1.1326 up 0.03826297 CER_Ceramide (d17:1, C24:0)0.8583 down 0.04035007 Isopalmitic acid (C16:0) 0.8489 down 0.04227044CE_Cholesterylester C18:3 0.8356 down 0.04430951 CE_CholesterylesterC22:6 0.8484 down 0.04599329 SM_Sphingomyelin (d17:1, C20:0) 0.9092 down0.06237979 Isoleucine 1.0817 up 0.06356105 Tyrosine 1.083 up 0.06681343Ornithine 1.0775 up 0.07275574 Phosphatidylcholine (C16:1, C18:2) 0.9234down 0.0730987 Mannose 1.1099 up 0.07913279 myo-Inositol 1.08 up0.08438868 (*1): free and from sphingolipids

TABLE 4c Metabolites of Table 4a which additionally showed a significantdifference (p-value <0.1) between HCMP patients with NYHA 1 scores andhealthy controls ratio of Metabolite_Name median regulation p-valueMaltose 2.3774 up 9.1877E−11 Cholesterylester C18:2 0.7422 down1.5121E−05 Taurine 1.3057 up 4.2799E−05 Cholesterylester C18:1 0.7566down 0.00017957 Isoleucine 1.1583 up 0.00067494 TAG (C16:0, C18:2)1.3413 up 0.00106071 Sarcosine 1.1148 up 0.00123586 SP_Sphinganine(d18:0) 1.3661 up 0.00126025 SP_Sphingosine (d18:1) 1.4493 up 0.00135359TAG (C16:0, C18:1, C18:2) 1.4301 up 0.00163138 CE_Cholesterylester C15:00.814 down 0.00544571 SM_Sphingomyelin (d18:1, C23:0) 0.9016 down0.00613976 Tricosanoic acid (C23:0) 0.8591 down 0.00645307SM_Sphingomyelin (d18:2, C23:0) 0.8856 down 0.00715908 Glycerol, lipidfraction 1.3643 up 0.00800466 Eicosenoic acid (C20:cis[11]1) 1.2284 up0.01113831 SM_Sphingomyelin (d17:1, C23:0) 0.8234 down 0.01457624 Uricacid 1.1278 up 0.01663987 beta-Carotene 0.7703 down 0.01772396 Serine,lipid fraction 1.2593 up 0.02396587 Testosterone 0.8387 down 0.03444244CE_Cholesterylester C22:5 0.8857 down 0.03705511 Noradrenaline(Norepinephrine) 1.1939 up 0.03929456 CE_Cholesterylester C22:4 0.8728down 0.04240014 1-Hydroxy-2-amino-(cis,trans)-3,5- 0.8851 down0.04256896 octadecadiene (from sphingolipids) Uridine 0.8639 down0.04452619 Glutamate 1.199 up 0.04801547 Lysophosphatidylcholine (C17:0)0.8984 down 0.04926739 SM_Sphingomyelin (d16:1, C23:0) 0.8842 down0.05494844 Cholesterylester C16:0 0.8449 down 0.06302395SM_Sphingomyelin (d18:1, C14:0) 0.9196 down 0.06434119 SM_Sphingomyelin(d17:1, C22:0) 0.9084 down 0.0655624 SM_Sphingomyelin (d18:2, C24:0)0.9168 down 0.06627783 Erythrol 1.112 up 0.06717477 Isocitrate 1.097 up0.06783798 SM_Sphingomyelin (d17:1, C20:0) 0.9104 down 0.06992485SM_Sphingomyelin (d17:1, C24:0) 0.9103 down 0.08265925 CER_Ceramide(d18:2, C14:0) 0.8865 down 0.08795584

TABLE 5 Metabolites with a significant difference (p-value <0.05) inexercise-induced change between CHF with NYHA score 1 and controlMetabolite ratio of median regulation p-value Glutamate 0.720 down0.025093 Hypoxanthine 0.407 down 0.034843 Phosphatidylcholine 1.011 up0.048864 (C18:0, C20:4)

TABLE 6 Metabolites with a significant difference (p-value <0.05)between patients with CHF with NYHA score I at the peak of exercise (t1)but not at rest (t0) Time point t0 t1 ratio of t0 t0 ratio of t1 t1Parameter median regulation p-value median regulation p-valuePhosphatidylcholine 1.035900639 up 0.339994 1.054274585 up 0.049492(C18:0, C20:4)

TABLE 7 Chemical/physical properties of selected analytes. Thesebiomarkers are characterized herein by chemical and physical properties.Metabolite Fragmentation pattern (GC-MS) and description GlycerolGlycerol phosphate, lipid fraction represents the sum phosphate,parameter of metabolites containing a glycerol-2- lipid fractionphosphate or a glycerol-3-phosphate moiety and being present in thelipid fraction after extraction and separation of the extract into apolar and a lipid fraction. 3-O- 3-O-Methylsphingosine exhibits thefollowing characteristic Methylsphingosine ionic fragments if detectedwith GC/MS, applying electron impact (EI) ionization mass spectrometry,after acidic methanolysis and derivatisation with 2% O-methylhydroxylamine-hydrochlorid in pyridine and sub- sequently withN-methyl-N-trimethylsilyltrifluoracetamid: MS (EI, 70 eV): m/z (%): 204(100), 73 (18), 205 (16), 206 (7), 354 (4), 442 (1). 5-O-5-O-Methylsphingosine exhibits the following characteristicMethylsphingosine ionic fragments if detected with GC/MS, applyingelectron impact (EI) ionization mass spectrometry, after acidicmethanolysis and derivatisation with 2% O-methylhydroxylamine-hydrochlorid in pyridine and sub- sequently withN-methyl-N-trimethylsilyltrifluoracetamid: MS (EI, 70 eV): m/z (%): 250(100), 73 (34), 251 (19), 354 (14), 355 (4), 442 (1). Phosphatidyl-Phosphatidylcholine No 02 represents the sum parameter choline No 02 ofphosphatidylcholines. It exhibits the following characteristic ionicspecies when detected with LC/MS, applying electro-spray ionization(ESI) mass spectrometry: mass-to-charge ratio (m/z) of the positivelycharged ionic species is 808.4 (+/− 0.5). TAG TAG (C16:0, C16:1)represents the sum parameter of (C16:0, C16:1) triacylglyceridescontaining the combination of a C16:0 fatty acid unit and a C16:1 fattyacid unit. It exhibits the following characteristic ionic species whendetected with LC/MS, applying electro-spray ionization (ESI) massspectrometry: mass-to-charge ratio (m/z) of the positively charged ionicspecies is 549.6 (+/− 0.5). TAG TAG (C16:0, C18:2) represents the sumparameter of (C16:0, C18:2) triacylglycerides containing the combinationof a C16:0 fatty acid unit and a C18:2 fatty acid unit. It exhibits thefollowing characteristic ionic species when detected with LC/MS,applying electro-spray ionization (ESI) mass spectrometry:mass-to-charge ratio (m/z) of the positively charged ionic species is575.6 (+/− 0.5). TAG TAG (C18:1, C18:2) represents the sum parameter of(C18:1, C18:2) triacylglycerides containing the combination of a C18:1fatty acid unit and a C18:2 fatty acid unit. It exhibits the followingcharacteristic ionic species when detected with LC/MS, applyingelectro-spray ionization (ESI) mass spectrometry: mass-to-charge ratio(m/z) of the positively charged ionic species is 601.6 (+/− 0.5). TAGTAG (C18:2, C18:2) represents the sum parameter of (C18:2, C18:2)triacylglycerides containing the combination of two C18:2 fatty acidunits. It exhibits the following characteristic ionic species whendetected with LC/MS, applying electro-spray ionization (ESI) massspectrometry: mass-to-charge ratio (m/z) of the positively charged ionicspecies is 599.6 (+/− 0.5). Cholestenol Cholestenol No 02 represents aCholestenol isomer. It No 02 exhibits the following characteristic ionicfragments if detected with GC/MS, applying electron impact (EI)ionization mass spectrometry, after acidic methanolysis andderivatisation with 2% O-methylhydroxylamine- hydrochlorid in pyridineand subsequently with N- methyl-N-trimethylsilyltrifluoracetamid: MS(EI, 70 eV): m/z (%): 143 (100), 458 (91), 73 (68), 81 (62), 95 (36),185 (23), 327 (23), 368 (20), 255 (15), 429 (15).

1-14. (canceled)
 15. A method for diagnosing heart failure in a subjectcomprising the steps of: a) determining in a sample of a subjectsuspected to suffer from heart failure the amount of at least onebiomarker selected from the group consisting of biomarkers listed inTable 1a to c, 3, 4a to c or 6; b) comparing the amount of said at leastone biomarker to a reference, whereby heart failure is to be diagnosed.16. The method of claim 15, wherein the heart failure is heart failureaccording to NYHA class I and the at least one biomarker is selectedfrom Table 4a to c or
 6. 17. The method of claim 15, wherein the sampleof the subject has been obtained under resting and wherein the at leastone biomarker is selected from Table 1a to c or 4a to c.
 18. The methodof claim 15, wherein the sample of the subject has been obtained underexercise and wherein the at least one biomarker is selected from Table 3or
 6. 19. The method of claim 15, wherein the reference is derived froma subject or group of subjects known not to suffer from heart failure ora calculated reference.
 20. The method of claim 19, wherein a differencebetween the reference and the amount of the at least one biomarker isindicative for heart failure.
 21. The method of claim 15, wherein thereference is derived from a subject or group of subjects known to sufferfrom heart failure.
 22. The method of claim 21, wherein a referencebeing identical with the at least one biomarker is indicative for heartfailure.
 23. The method of claim 15, wherein the heart failure iscongestive heart failure.
 24. The method of claim 15, wherein thesubject suffers from dilated cardiomyopathy, ischemic cardiomyopathy,and/or hypertrophic cardiomyopathy.
 25. A method for identifying whethera subject is in need for a therapy of heart failure comprising the stepsof the method of claim 15, and a further step of identifying a subjectin need for therapy if heart failure is diagnosed.
 26. A method fordetermining whether a therapy against heart failure is successful in asubject comprising the steps of the method of claim 15, and a furtherstep of determining whether a therapy is successful if no heart failureis diagnosed.
 27. A method for diagnosing heart failure in a subjectcomprising the steps of: a) determining in a first and a second sampleof a subject suspected to suffer from heart failure the amount of atleast one biomarker selected from the group consisting of biomarkerslisted in Table 2 or 5, wherein said first sample has been obtained atrest and said second sample has been obtained under exercise; b)calculating a ratio of the amount of the at least one biomarkerdetermined in the first and the second sample; and c) comparing thecalculated ratio to a reference, whereby heart failure is to bediagnosed.
 28. The method of claim 27, wherein the heart failure isheart failure according to NYHA class I and the at least one biomarkeris selected from Table
 5. 29. The method of claim 27, wherein thereference is derived from a subject or group of subjects known not tosuffer from heart failure or a calculated reference.
 30. The method ofclaim 29, wherein a difference between the reference and the amount ofthe at least one biomarker is indicative for heart failure.
 31. Themethod of claim 27, wherein the reference is derived from a subject orgroup of subjects known to suffer from heart failure.
 32. The method ofclaim 31, wherein a reference being identical with the at least onebiomarker is indicative for heart failure.
 33. The method of claim 27,wherein the heart failure is congestive heart failure.
 34. The method ofclaim 27, wherein the subject suffers from dilated cardiomyopathy,ischemic cardiomyopathy, and/or hypertrophic cardiomyopathy.
 35. Amethod for identifying whether a subject is in need for a therapy ofheart failure comprising the steps of the method of claim 27, and afurther step of identifying a subject in need for therapy if heartfailure is diagnosed.
 36. A method for determining whether a therapyagainst heart failure is successful in a subject comprising the steps ofthe method of claim 27, and a further step of determining whether atherapy is successful if no heart failure is diagnosed.